The Institute for Business and Finance Research for Inclusion in Global Conference on Business and Finance 2012

The International Journal of Business and Finance ESEARCH VOLUMER 6 NUMBER 4 2012

CONTENTS

Volatility and Compounding Effects on Beta and Returns 1 William J. Trainor Jr

Do Small and Medium Sized Enterprises Match their Assets and Liabilities? Evidence from Portugal 13 Jan Bartholdy, Cesario Mateus & Dennis Olson

Convenience Yields in Bulk Commodities: The Case of Thermal Coal 33 Jason West

International Evidence on Market Linkages after the 2008 Stock Market Crash 45 Gulser Meric, Christine Lentz, Wayne Smeltz & Ilhan Meric

The Price Response to Nikkei 225 Stocks Index Adjustments 59 Chia-Jung Tu

The Price of Stocks in Latin American Financial Markets: An Empirical Application of the Ohlson Model 73 Pedro Martínez, Diego Prior & Josep Rialp

Forecasting Term Structure of HIBOR Swap Rates 87 Mei-Mei Kuo, Shih-Wen Tai & Bing-Huei Lin

Cost Efficiency of French Commercial Banks: Domestic versus Foreign Banks 101 Raoudha Béjaoui Rouissi & Houssam Bouzgarrou

Analysis of Extreme Dependence Between Istanbul Stock Exchange and Oil Returns 113 Gözde Ünal & Derya Korman

Integration of Key Worldwide Money Market Interest Rates and The Federal Funds 125 Rate: An Empirical Investigation Krishna M. Kasibhatla

The International Journal of Business and Finance Research ♦ VOLUME 6 ♦ NUMBER 4 ♦ 2012

VOLATILITY AND COMPOUNDING EFFECTS ON BETA AND RETURNS William J. Trainor Jr, East Tennessee State University

ABSTRACT

Previous research indicates that long-term investors are not compensated for beta or volatility risk. This study shows these two results are at least partly due to the mathematics of compounding exacerbated in high volatility markets. Theoretical beta portfolios defined to perform exactly as the Capital Asset Pricing Model (CAPM) would predict on a monthly basis, show that high beta portfolios dramatically outperform in low volatility environments and underperform in high volatility environments. Empirically sorted beta portfolios confirm the results and show in a low volatility environment, high beta portfolios outperform low beta portfolios by 0.42% a month and underperform by 0.51% in high volatility environments. When combining the two market environments, the inevitable result shows no relationship between beta and return.

JEL: G11

KEYWORDS: Beta, Compounding, Volatility

INTRODUCTION

he single factor market model, and by extension, the capital asset pricing model (CAPM) is one of the most tested models in finance. Originally set forth by Sharpe (1964), Litner (1965) and T Mossin (1966), the model concludes with the simplest proposition in finance: greater risk should be compensated with a greater return over time. However, over the last 30+ years, empirical validation of this premise has been stymied. One crucial overlooked factor often referred to as the compounding problem help explains why this has been the case.

The problem can be elucidated by considering the following: Assume a 0% risk free rate and an index return that falls 10% in period 1 and increases 12% in period 2. Over both periods, the index has a cumulative return of 0.8%. Now consider a 2.0 beta portfolio. It falls 20% in period 1 and increases 24% in period 2. Over both periods, its cumulative return is -0.8%. A 0.8 beta portfolio actually returns 0.83% over the two periods. Thus, over the two periods, the high beta portfolio underperforms the market and a 0.8 beta portfolio actually outperforms the market despite the overall positive market return. This compounding problem is exacerbated through time the higher the beta and the greater the level of volatility.

Now consider the CAPM. Traditional testing of the CAPM first employed by Fama & Macbeth (1973) tests whether the coefficient on a regression running the excess return of the stock against beta is significant and positive. The regression takes the general form of

R R = α + γ β + µ (1)

it − ft t t i i where Rit - Rft is the return on the ith stock minus the risk-free rate and αt and γt are regression coefficients. Betas (β) are calculated based on time period t-1 returns and the above regression is run to see if beta is significantly related to excess returns in time t, i.e. is γt positive and significantly different from zero.

W. J. Trainor | IJBFR ♦ Vol. 6 ♦ No. 4 ♦ 2012

The inherent problem with this is that monthly returns are usually used to calculate betas. Thus, empirical betas to some extent are just a measure of relative monthly volatility. The greater the relative volatility for a given stock, the greater its beta and theoretically, it should be associated with higher returns. However, the higher the beta, the greater the compounding problem will be when calculating a return over an extended period. In periods of very little volatility, beta should positively relate to excess returns. In periods of high volatility, the compounding problem will be the deciding factor and higher beta stocks will not demonstrate higher returns even if the index return is positive.

The findings of this study reconfirm that low beta portfolios perform just as well and even outperform high beta portfolios over long time horizons. However, in low volatility markets, high beta portfolios significantly outperform low beta portfolios. In contrast, the returns of high beta portfolios are significantly negative in high volatility markets. The compounding problem easily explains this phenomenon.

Thus, beta appears to be a relevant risk factor only for short-term and more active investors. Over longer horizons, high beta portfolio’s significant losses during volatile periods offset their outperformance in low volatility environments. These losses cause the long-run beta-return relationship to break down due to the simple mathematics of compounding.

The remainder of this paper organizes as follows: A short literature review precedes the derivation of a mathematical model showing how the compounding problem affects higher beta portfolios. A description of the data and methodology follows. Thereafter, the results section demonstrates the magnitude of the compounding problem using "perfect" monthly betas and empirically shows how beta and volatility sorted portfolios perform in both high and low volatility environments. A simple ex ante trading model is also tested. The paper finishes with some concluding comments.

LITERATURE REVIEW

Although the compounding problem highlighted above seems relatively obvious, the havoc it causes did not garner serious scrutiny until the significance of its effect manifested itself in the recently created leveraged ETF market. With funds creating daily betas up to 3.0, the effects of compounding condensed into a relatively short period. Despite near perfect daily betas, leveraged funds had dismal annual performance regardless of underlying index returns. Several studies expounded on this issue and specifically identified compounding as the singular most important explanation, (Trainor & Baryla, 2008; Carver, 2009; Avellaneda & Zhang, 2009; Lu, Wang, & Zhang, 2009; Cheng & Madhavan, 2009).

Although betas discussed in the leveraged ETF market are absolute betas since they multiply the actual index return and not the excess return relative to the risk-free rate, the compounding problem can be just as serious when employing the CAPM. Studies running the gamut from Fama & MacBeth (1973) to Fama & French (1992) conclude that the CAPM does not empirically hold. This even led Hulbert (1992) to exclaim, "Beta is dead." Fama & French (2004) continue to reiterate this conclusion although the debate continues, (Grauer & Janmaat, 2009). These studies do not identify the compounding problem.

Although not expressly recognized, Pettengill, Sundaram, & Mathur (1995, 2002) mitigate the compounding issue when they tested beta by separating the market into up and down monthly return periods. Not surprisingly, they show that beta actually explains 70% or more of the deviation across portfolios. Their explanation centered on the difference between ex-ante expectations and ex-post realizations, but unwittingly, they also simultaneously eliminated most of the compounding problem by not combining the up months with the down.

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More recently, it has also been shown that low volatility and low beta stocks outperform high volatility and high beta stocks by more than 1% a month, (Ang, Hodrick, Xing, & Zhang, 2006, 2009; Baker, Bradley & Wurgler, 2011). Once again, this phenomenon appears to have its roots within the compounding problem and Baker, Bradley & Wurgler (2011) even make reference to this issue but do not develop the idea to any extent.

Thus, stocks or portfolios may be doing exactly what their betas imply they do given market volatility issues. If one measures beta on a monthly basis, it should not be surprising that its usefulness and relationship to return is also going to be on a short-term basis. Although the market has averaged a positive return over time, both the length of time studied and the underlying volatility has broken the link between beta and long-run returns. The next section demonstrates the theoretical underpinnings of why this is the case.

THE COMPOUNDING PROBLEM

To demonstrate the compounding problem, assume the market portfolio (M) follows a geometric Brownian motion. From Ito's lemma, it follows that: dM/M = + dW (2) where µ is theµdt mean,σ σ is volatility, and W is standardized Brownian motion.

The continuously compounded return for the market from i =0 to t is:

M = /2 (3) 2 Rt t t Now considerµ − σany portfolio with a return perfectly related to the market by beta (B). The return model for this portfolio (P) is:

dP/P = dM/M = + dW (4)

The cumulativeβ compoundedβµdt returnβσ for this portfolio from i = 0 to t is then:

P = /2 (5) 2 2 Rt t t This simplyβµ − showsβ σ that the return and standard deviation of the return process is some beta multiple of the market portfolio. If the portfolio continuously follows the market relative to its beta, then the relationship between PRt and βMRt is

P M = [( )/2] (6) 2 2 Rt Rt t This− showsβ for any− positiveβ − β variance,σ that the portfolio will not maintain a return that is beta times the underlying index return. The greater the variance and the higher the beta, the faster this relationship degenerates. In absolute terms, the return difference between the portfolio and the market is:

P M = ( 1) + (1 ) /2 (7) 2 2 Rt Rt t t This− states that forβ −any givenµ positive− β marketσ return, if the volatility is high enough, the cumulative return for a portfolio with β > 1 will be less than the market portfolio.

W. J. Trainor | IJBFR ♦ Vol. 6 ♦ No. 4 ♦ 2012

Figure 1: Annualized Return Difference 15.00%

10.00%

5.00%

0.00% 1.5 Beta 2.0 Beta -5.00% 2.5 Beta

Annual Return Difference Return Annual -10.00%

-15.00% 10% 20% 25% 30% Annual Standard Deviation

This figure shows the difference in returns between beta portfolios and the index for standard deviations ranging from 10 to 30%.

Using Equation (7), Figure 1 shows the difference in returns between portfolios with betas of 1.5, 2.0 and 2.5 relative to the index over a year assuming an 8% cumulative annualized return. As Figure 1 shows, the relationship between beta and return turns negative as volatility levels increase. A 2.5 beta portfolio will underperform the index when volatility exceeds 21% and a 1.5 beta portfolio underperforms when volatility reaches 25%.

2 2 As a numerical example, if β = 2.0, then equation (7) is equal to µt - 1.5σt . Thus, when µt = 1.5σt there 2 will be no relationship between beta and return and when µt < 1.5σt , the relationship will actually be negative. To quantify this further, if the annualized market standard deviation is 30%, then 1.5 times the daily volatility is equal to 0.00036 assuming 250 trading days in a year. A daily return of 0.036% gives a cumulative annualized return of 9.4% and under these conditions, a portfolio with a β = 2.0 will have zero excess return relative to the market. Any return less than this will result in a 2.0 beta portfolio underperforming the index.

Although the CAPM is by definition a single period model, in reality both beta and a portfolio's return are measured over time. The results above clearly show how the compounding problem causes the expected positive relationship between beta and return to break down as volatility increases or market returns decrease.

DATA AND METHODOLOGY

Although the long-term empirical relationship between beta and returns appears to be non-existent, part of the reason could be due to beta drift after creating portfolios. To eliminate this issue among others that plague beta sorted portfolios, theoretical beta portfolios ranging from 0.5 to 3.0 are created so that their returns are always equal to exactly what the CAPM would imply on a monthly basis. The actual return each month of the Center for Research in Security Price's (CRSP) value weighted index from January 1926 to December 2009 is used as the market proxy and the 30-day t-bill rate is used for the risk-free rate.

Excess returns for each beta portfolio are regressed on the actual beta values using equation (1) described earlier. In addition, market periods are separated into high and low volatility environments based on whether they are above or below the average volatility. For the CRSP value weighted index, the average standard deviation from 1926-2009 is 15.8%. Welch's (1947) t-test for the difference in means between

The International Journal of Business and Finance Research ♦ VOLUME 6 ♦ NUMBER 4 ♦ 2012

volatility environments is conducted for each beta ranked portfolio. This test is appropriate since the comparison of means occurs between different volatility environments. The actual test statistic is:

= (8) x�1 − x�2 2 2 푡 s1 s2 �n1+n2 where x and x are the sample means and s and s are the sample variances. 2 2 Additionally,�1 �t2he same procedure above is 1applied2 to CRSP's NYSE/AMEX Scholes-William sorted beta portfolios and volatility sorted portfolios from 1970 to 2009. The smallest and largest beta and volatility portfolios are not used due to extreme values.

RESULTS

Theoretical Beta Portfolios

Table 1 shows the cumulative value of a $1 investment beginning in 1926 for the various beta portfolios. By December 2009, a $1 investment in the index is worth $2,286. For a 2.0 beta portfolio, this value reaches $12,007. Thus, in theory, seeking out higher beta portfolios could lead to vastly superior returns. However, note that a 2.5 beta portfolio only accumulates to $6,982 and a 3.0 beta portfolio only accumulates to $1,093. Thus, for long-term investors, the compounding issue clearly shows that investing in portfolios with significantly higher betas is not associated with higher returns. This result is robust as it holds for the last 40 years as well.

Table 1: Theoretical Beta Returns

Beta 0.5 Beta 1.0 Beta 1.5 Beta 2.0 Beta 2.5 Beta 3.0 1926 -2009 Cum. Value $311 $2,286 $7,900 $12,007 $6,982 $1,093 1926 -2009 Annual Ret. 7.07% 9.64% 11.28% 11.83% 11.11% 8.69% 1970 -2009 Annual Ret. 8.19% 10.07% 11.28% 11.75% 11.41% 10.15% This table shows the average cumulative value along with the annualized return for the CRSP Value Weighted index.

On a shorter-term basis, Table 2 shows the rolling annual average returns across these beta portfolios. The results show a monotonically increasing relationship between beta and return. Thus, for yearly time horizons, there is a theoretical positive relationship between beta and returns. However, portfolios with betas of 2.0 and greater are associated with extreme risk as all suffer annual losses on at least one occasion of more than 90%. Despite the fact the overall rolling average annual return increases for higher beta portfolios, the compounding issue over longer time horizons not only mitigates, but also overwhelms the shorter-term higher returns.

Because higher levels of volatility exacerbate the compounding problem, it is of note to see what occurs to beta portfolios during periods of above and below average volatility. Table 2 shows the advantage of owning higher beta portfolios during periods of below average volatility. While the index returns 15.83%, a 3.0 beta portfolio returns 46.70%. The results are qualitatively the same for the last 40 years as well.

W. J. Trainor | IJBFR ♦ Vol. 6 ♦ No. 4 ♦ 2012

Table 2: Theoretical Beta Annual Returns

Beta 0.5 Beta 1.0 Beta 1.5 Beta 2.0 Beta 2.5 Beta 3.0 1926 - 2009 Average Annual Ret. 7.57% 11.71% 16.14% 20.88% 25.90% 31.18% Min. Annual Ret. -39.21% -65.84% -82.42% -92.01% -97.03% -99.28% Max. Annual Ret. 68.40% 156.35% 260.22% 372.36% 481.57% 573.90% Low Vol. Annual Ret. 9.54% 15.83% 22.66% 30.05% 38.05% 46.70% High Vol. Annual Ret. 2.94% 2.02% 0.86% -0.64% -2.62% -5.22% t-test for difference between means 3.83*** 13.81*** 8.32*** 8.79*** 9.36*** 10.06*** 1970 - 2009 Average Annual Ret. 8.31% 10.92% 13.62% 16.40% 19.24% 22.12% Low Vol. Annual Ret. 9.36% 13.35% 17.60% 22.11% 26.87% 31.90% High Vol. Annual Ret. 5.93% 5.40% 4.61% 3.48% 1.96% -0.03% t-test for difference between means 3.33*** 4.17*** 4.68*** 5.12*** 5.55*** 5.98*** This table shows annual rolling returns using the CRSP Value Weighted index for theoretical beta portfolios ranging from 0.5 to 3.0. High volatility periods are defined as periods greater than the average for the entire period while low volatility periods are those periods less than the average. Volatility is defined as the annualized standard deviation of 12 monthly returns. ***Significant at the 1% level.

Conversely, during periods of above average volatility, returns decline monotonically from 2.94% for a 0.5 beta portfolio to -5.22% for a 3.0 beta portfolio. These results also hold over the last 40 years with returns falling from 5.93% to -0.03%. In addition, for every beta portfolio, mean returns are significantly greater during low volatility periods and significantly lower during high volatility periods. Welch's t-test confirms this with t-values ranging from 3.33 to 13.81, all of which are significant at the 1% level.

Table 3: Theoretical Beta Regression Results

Coefficient Estimate R-square 1926-2009 Constant Beta (t-stat) Avg. Excess Return -0.0129 0.0945 (44.94)*** 99.8 Low Vol. Excess Return -0.0250 0.1485 (34.53)*** 99.6 High Vol. Excess Return 0.0107 -0.0311 (-8.49)*** 96.4 1970-2009 Constant Beta (t-stat) Avg. Excess Return -0.0026 0.0553 (110.34)*** 99.9 Low Vol. Excess Return -0.0127 0.0902 (47.74)*** 99.9 High Vol. Excess Return 0.0200 -0.0236 (-8.81)*** 95.1 This table shows the regression results based on equation (1) which regresses the excess annual returns derived from theoretical beta portfolios on beta. ***Significant at the 1% level.

Table 3 shows the regression results based on equation (1) relating beta to excess returns. These results statistically confirm beta is positively related to excess returns in low volatility periods and significantly negative during high volatility periods at the 1% level. It is also apparent the overall positive annual relationship is driven solely by the magnitude of the positive relationship during low volatility periods. For the overall period, a 0.1 increase in portfolio beta is associated with a 1.485% increase in excess return during low volatility periods. However, this is offset by a 0.311% decrease in high volatility environments. For long-term investors, these swings in returns and the mathematics of compounding cause the annual positive relationship to disintegrate over time.

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Beta Sorted Portfolios

The results above show how even theoretically derived portfolios that produce perfect monthly returns in accordance with the CAPM can still show a negative relationship between beta and returns when volatility is high. Empirically derived beta portfolios suffer from the same compounding issue and are subject to even further error due to beta instability and the unique returns to the component stocks in each portfolio.

Table 4 shows the average monthly returns for the CRSP NYSE/AMEX Scholes-William sorted beta portfolios from 1970 to 2009. Portfolios rank from smallest to largest and beta values range from 0.67 to 1.42. Confirming previous research, there is no discernible relationship between beta and returns. The regression results in Table 5 show the coefficient for beta equal to 0.0003 with an insignificant t-stat of 0.57 and an r-square of only 5.1%. It should be noted that due to survivorship bias and the equal weighting used for the components stocks, the monthly returns for all the portfolios are quite high.

Table 4: Empirical Beta Sorted Portfolio Monthly Returns

Beta 1 Beta 2 Beta 3 Beta 4 Beta 5 Beta 6 Beta 7 Beta 8 Beta 0.67 0.83 0.93 1.03 1.11 1.20 1.28 1.42 Return 1.52% 1.59% 1.63% 1.60% 1.53% 1.57% 1.60% 1.58% Low Vol. 1.95% 2.14% 2.26% 2.21% 2.24% 2.27% 2.35% 2.37% High Vol. 0.85% 0.72% 0.64% 0.64% 0.43% 0.47% 0.43% 0.34% t-test for difference between means 2.56** 2.88*** 3.05*** 2.77*** 3.03*** 2.78*** 2.79*** 2.63*** This table shows monthly average returns from January 1970 to December 2009 for Scholes-William sorted beta portfolios. High volatility periods are defined as periods greater than the average for the entire period while low volatility periods are those less than the average. Volatility is defined as the annualized standard deviation of daily returns within each month. ***Significant at the 1% level;**5% level.

Table 5: Beta Sorted Regression Results

Constant Beta (t-stat) R-square Avg. Excess Return 0.0108 0.0003 (0.57) 5.1% Low Vol. Excess Return 0.0124 0.0050 (5.65)*** 93.2% High Vol. Excess Return 0.0083 -0.0068 (-9.11)*** 91.2% This table shows the regression results based on equation (1) which regress the average excess monthly returns derived from beta sorted portfolios on beta. ***Significant at the 1% level.

During periods of low volatility, the advantage of owning higher beta portfolios is clear as the lowest beta portfolio returns 1.95% while the highest beta portfolio returns an average of 2.37%. This may not appear economically significant, but on an annual basis, it is 5% greater and the regression results shown in Table 5 confirm beta is significant at the 1% level with a t-stat of 5.65. During high volatility environments, portfolio returns monotonically decrease from 0.85% to 0.34% per month. In this case, beta is negatively statistically significant at the 1% level with a t-stat of -9.1. Both of these regressions show beta explains more than 90% of the difference in excess returns across beta portfolios. This reaffirms the theoretical results in the previous section. In addition, at every beta level, the low volatility return exceeds the high volatility return in both an economic and statistically significant sense at the 1% or 5% level with t-stats ranging from 2.56 to 3.05. Thus, Tables 4 and 5 also show that the relationship between beta and returns is not dead.

Volatility Sorted Portfolios

The correlation between volatility sorted portfolios and beta is quite high. Table 6 shows as the standard deviation of the portfolios increases from 12.06% to 26.21%, the corresponding beta of these portfolios

W. J. Trainor | IJBFR ♦ Vol. 6 ♦ No. 4 ♦ 2012

increases from 0.71 to 1.37. The average monthly return actually shows that higher volatility portfolios are associated with higher returns. Regressing the returns on standard deviations confirms that this relationship is statistically significant at the 1% level with a 6.6 t-stat, (see Table 7). This is interesting in that these portfolios also have higher betas, which suggests a general positive relationship between beta and return. This is contrary to the lack of relationship found between beta and return for portfolios sorted by beta.

Reaffirming the results from Table 4, high volatility and by association high beta portfolios, outperform their low volatility/low beta counterparts during periods of low volatility. At every ranked standard deviation portfolio level, low volatility portfolios significantly outperform high volatility portfolios with t-stats ranging from 2.28 to 3.39, all of which are significant at the 5% or 1% level.

Comparing returns across ranked standard deviation portfolios in the two volatility environments, Table 7 shows that portfolio returns in high volatility periods are not significantly associated with portfolio standard deviation levels, t-stat of 0.33. During low volatility environments, the relationship is pronounced and significant with a t-stat of 6.39.

Table 6: Volatility Sorted Portfolio Monthly Returns

Vol. 1 Vol. 2 Vol. 3 Vol.4 Vol.5 Vol. 6 Vol. 7 Vol. 8 St. Dev. 12.06% 14.07% 15.79% 17.27% 18.94% 20.87% 23.44% 26.21% Beta 0.71 0.86 0.97 1.05 1.12 1.21 1.31 1.37 Return 1.10% 1.20% 1.26% 1.27% 1.36% 1.40% 1.35% 1.61% Low Vol. 1.58% 1.79% 1.90% 2.01% 2.11% 2.15% 2.08% 2.36% High Vol. 0.34% 0.28% 0.26% 0.12% 0.18% 0.23% 0.19% 0.43% t-test for difference between means 3.20*** 3.35*** 3.25*** 3.39*** 3.18*** 2.87*** 2.52** 2.28** This table shows monthly average returns from January 1970 to December 2009 using CRSP's NYSE/AMEX standard deviation ranked portfolios. High volatility periods are defined as periods greater than the average for the entire period while low volatility periods are those less than the average. Volatility is defined as the annualized standard deviation of daily returns within each month. ***Significant at the 1% level.

Table 7: Volatility Sorted Regression Results

Constant Volatility (t-stat) R-square Avg. Excess Return 0.0030 0.0299 (6.60)*** 87.8% Low Vol. Excess Return 0.0067 0.0468 (6.39)*** 87.2% High Vol. Excess Return -0.0026 -0.0027 (0.33) 13.2% This table shows the regression results based on equation (1) which regress the average excess monthly returns derived from volatility sorted portfolios on the standard deviation. ***Significant at the 1% level.

Thus, volatility sorted portfolios do show a positive relationship between volatility and return, although it appears most of this is due to the outperformance of high volatility portfolios during below average volatility periods. This provides additional evidence that the compounding problem is an issue but not to the extent, it is in beta-sorted portfolios. It should also be pointed out that these are average monthly returns and do not necessarily imply that a long-term buy-and-hold strategy of high volatility portfolios will outperform. On the contrary, they are more likely to underperform due to the same compounding issue that plagues beta-sorted portfolios.

Ex-Ante Trading

The evidence above shows that high beta portfolios payoff during low volatility periods. However, this is based on ex-post volatility measurements. To the extent volatility is persistence (Mandelbrot, 1963; Engle, 1982; Bollerslev, 1986; Kritzman, 2010) and thus predictable, it may be possible to exploit this

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relationship and find an ex ante reason to hold high beta portfolios. A simple trading rule based on the volatility phenomena is to hold the particular beta ranked portfolio if the previous month's volatility is less than the average volatility over the preceding 5 years. Otherwise, hold the lowest ranked beta portfolio. The risk-free asset is not used so the risk differential between the trading strategy and the buy and hold position is more comparable.

Table 8 shows the results for this type of strategy for each beta-sorted portfolio. Unfortunately, this simple trading rule does not suggest moving into high beta portfolios based on ex ante volatility levels will increase returns. There is no statistical difference between using the trading rule and the buy and hold strategy at any beta level. In addition, there is no statistically significant relationship between beta and excess return using the trading rule, t-stat of -1.05 on the beta coefficient using equation (1). It is possible that advanced predictive models of volatility or the use of the Chicago Board Option Exchange’s Volatility Index (VIX) will help. However, the results here suggest long-term investors with no priori expectations on future volatility levels are better off investing in low beta portfolios.

Table 8: Volatility Based Trading Returns

Beta 1 Beta 2 Beta 3 Beta 4 Beta 5 Beta 6 Beta 7 Beta 8 Beta 0.67 0.83 0.93 1.03 1.11 1.20 1.28 1.42 Buy and Hold 1.52% 1.59% 1.63% 1.60% 1.53% 1.57% 1.60% 1.58% Trading Rule 1.52% 1.60% 1.61% 1.56% 1.52% 1.52% 1.53% 1.53% t-test for difference between means 0.00 0.03 0.06 0.12 0.03 0.14 0.18 0.12 This table shows average monthly returns from 1970 to 2009 that compare a buy-and-hold strategy to a trading rule that involves holding the particular beta sorted portfolio when the previous month's volatility is less than the preceding 60-month average, else hold the lowest beta sorted portfolio.

CONCLUDING COMMENTS

The evidence against a positive relationship between long-run returns and beta is substantial. This study demonstrates that even theoretically derived beta portfolios that explain 100% of the deviation in returns on a monthly basis will not show a positive relationship between beta and return over longer horizons due to the mathematics of compounding. Based on historical returns and volatility, portfolios with betas greater than two appear doomed to underperform over extended holding periods. However, for shorter investment horizons and especially within low volatility environments, there is a rationale for holding higher beta portfolios.

Theoretically, there is a pronounced positive relationship between beta and return in low volatility environments where the effects of compounding are mitigated. From 1926 to 2009, the annualized return in low volatility environments for a 3.0 beta portfolio is 46.7% compared to -5.3% in above average volatility environments. This relationship remains over the last 40 years, and the last 20 as well.

Empirically sorted beta portfolios show the same qualitative type of outperformance in low volatility environments and significant underperformance in high volatility environments. Compounding is clearly an issue in explaining the breakdown between beta and return over longer periods. For portfolios sorted by volatility, the results are not as clear. However, the outperformance of high volatility portfolios appears to be contained within below average volatility environments reinforcing the idea that the compounding problem is a major issue.

Thus, it is the contention of this study that using beta to estimate the risk of a portfolio does have merit. In highly volatile markets when the cost of holding risky portfolios is high, high beta portfolio values fall. However, during low volatility environments, high beta portfolios do realize excess returns.

W. J. Trainor | IJBFR ♦ Vol. 6 ♦ No. 4 ♦ 2012

Unfortunately, a simple trading rule based on ex-ante volatility levels does not appear able to exploit this relationship. More advanced models to predict volatility may be more successful. This study is unable to demonstrate any ex ante rationale for long-term investors to hold high beta portfolios. For those with specific volatility forecasts and short-term horizons, the beta values of portfolios are relevant. If an investor is convinced volatility levels will remain low, investing in high beta portfolios should be associated with higher returns.

Although this study shows the compounding problem is an issue for cumulative returns, it is unable to explain why the monthly average returns of higher beta portfolios are not associated with higher returns. Thus, other factors still plague the empirical results regarding the CAPM. In addition, the return calculations used in this study ignore transaction costs and both CRSP's beta and volatility-sorted portfolios suffer from serious survival bias.

Moving forward, the effects of compounding in other markets remain unexplored. This issue is especially relevant for ETFs that have specialized in narrower markets that are more volatile. In addition, to the extent, the return degradation due to the compounding effect over any horizon can be isolated, a more enlightened answer may be forthcoming as to why size and book-to-market effects are so pronounced. Are these effects consistent regardless of the volatility environment? Finally, future research that controls for the compounding effect along with the use of the CAPM may improve portfolio performance evaluation and give a more accurate measure of manager derived excess return.

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Ang, Andrew, R. Hodrick, Y. Xing, & X. Zhang (2009) “High Idiosyncratic Volatility and Low Returns: International and Further U.S. Evidence,” Journal of Financial Economics, 91, 1, pp. 1-23

Avellaneda, M. & S. Zhang (2009), "Path-dependence of leveraged ETF returns," Available at SSRN: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1404708, 19 May.

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BIOGRAPHY

William Trainor is an associate professor at East Tennessee State University and a CFA Charterholder. He has written more than 30 papers on investments including publications in the Journal of Investing, Financial Services Review, Journal of Financial Planning, Journal of Personal Finance, and Financial Review. He can be reached at ETSU, Dept. of Economics and Finance, Box 70686, Johnson City, TN, 37614-1709, [email protected].

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DO SMALL AND MEDIUM SIZED ENTERPRISES MATCH THEIR ASSETS AND LIABILITIES? EVIDENCE FROM PORTUGAL Jan Bartholdy, University of Aarhus Cesario Mateus, University of Greenwich Dennis Olson, American University of Sharjah

ABSTRACT

For small and medium-sized enterprises, various types of debt are not identical. There are specific costs and benefits associated with each funding source. We argue that the asset and liability sides of the balance sheet are interrelated. Specifically, we hypothesize that firms match specific assets with a specific set of liabilities. We test our theory using a unique sample of Portuguese firms for the years 1990-2000. Our data set identifies various short-term and long-term funding sources, as well as the uses of these funds to purchase various assets. Our results reject independence between the two sides of the balance sheet—suggesting that small and medium-sized firms in Portugal do indeed match specific assets with specific liabilities. The implication for financial theory is that each asset or project may have a different weighted average cost of capital. That is, there is no single weighted average cost of capital for a typical small to medium-sized firm.

JEL: G32, M40

KEYWORDS: Asset-liability matching, SMEs, capital structure, sources and uses of funds

INTRODUCTION

ost of the literature on capital structure has implicitly assumed that the choice between debt and equity depends solely on firm characteristics, or the firm’s demand for debt. For example, MRajan and Zingales (1995) and Booth et al (2001) focus on the demand side of capital structure for large listed firms. However, Faulkender and Petersen (2005, p. 46) have shown that a firm’s debt- equity structure depends “not only on the determinants of its preferred leverage (the demand side) but also the variables that measure the constraints on a firm’s ability to increase its leverage (the supply side).” Also, as noted by Stowe, Watson, and Robertson (1980, p. 973), “the actual balance sheets of modern corporations do not exhibit an independence between the two sides of the balance sheet.”

Although the finance literature has recognized the interrelationship between the two sides of the balance sheet, the implications have received little attention. It means the cost of capital can vary between assets, so that a firm need not have a single weighted average cost of capital (WACC). While asset and liability interdependence may not be so important for large corporations, capital constraints and differential costs between the sources of debt capital can have a major impact on small and medium enterprises (SMEs). For SMEs, the cost of funds may vary on a project-by-project basis depending on project size, riskiness, and time horizon. Each source of debt conveys its own particular set of costs and benefits and a firm may choose a different mix of funding sources for each asset it purchases. In this paper, we propose that the two sides of the balance sheet of SMEs are interdependent causing them to match specific assets with specific liabilities. The firm’s optimal capital structure then depends on the assets they purchase.

We empirically test our theory of asset and liability matching using a unique sample of 1416 Portuguese industrial SMEs over the years 1990-2000. This data set provides detailed information about sources of SME funding—including internal equity, bank loans, trade credits, non-bank loans, leasing, and other

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short-term debt. For these SMEs, we test for independence between sources of funding and the uses of funds to purchase various assets. Our tests reject independence, suggesting the asset and liability sides of the balance sheets are interrelated. Each asset class has its own unique mix of financing sources. Our results suggest that all types of debt are important for SMEs and that empirical work in finance should distinguish between various types of debt. Thus, a firm does not have a unique average weighted cost of capital and decisions about capital structure become more complicated than in traditional analysis. The debt portion of the debt-equity ratio depends upon the type of debt a firm uses.

The remainder of the paper proceeds as follows. The next section provides a brief literature review, the third section describes the data sample, and section 4 discusses sources and uses of funding. The fifth section shows how we distinguished between cheap and expensive trade credits, section 6 presents the empirical results, and section 7 concludes the study.

LITERATURE REVIEW

In a world of frictionless capital markets with no asymmetric information or agency costs, even small firms can fund all of their positive net present value projects. However, the presence of asymmetric information, as stated by Fama (1985), James (1987), and Carey, Post, and Sharpe (1998), means that the firm knows more about the quality of their own projects than outside lenders. As discussed by Leland and Pyle (1977), Diamond (1984), Ramakrishnan and Thakor (1984), Fama (1985), Haubrich (1989), and Diamond (1991), this problem has encouraged the development of specialized or differentiated financial markets and institutions.

Different institutions specialize in extending credit to various firms and banks have some clear advantages in solving the asymmetric information problem for small firms. Mester, Nakamura, and Renault (2001) mention that banks involved in the payment function often know cash inflows before the firms do. Hoshi, Kashyap, and Scharfstein (1990a, 1990b), Petersen and Rajan (1994, 1995) and Berger and Udell (1998) have documented the importance of such relationships between lenders and borrowers and the impact on the cost of borrowing.

The advantages of banking relationships are more important for small firms than for large firms. More information is public for large firms, they often have more than one banking relationship, and many of these firms have access to bond financing. Since bonds come with high fixed costs and lower interest rates than bank loans, Faulkender and Petersen (2006) state that large firms are more likely to borrow from financial markets than from financial institutions. Financial institutions also have advantages in solving moral hazard problems (ex-post contractual problems). By offering both short-term lines of credit and long-term loans, banks can withdraw funds and/or renegotiate the conditions and interest rates if the firm engages in “moral hazard” actions (risk shifting etc.). Creditors in financial markets, on the other hand, have to rely on covenants negotiated ex-ante since it is nearly impossible to renegotiate the terms of corporate bonds ex-post. To the extent that banks are successful ex-post monitors and reduce the moral hazard problems, then bank debt becomes the preferred source of external capital for small firms.

Rajan (1992), Bolton and Scharfstein (1996), and Bolton and Freixas (2000) noted that different institutions have comparative advantages in resolving financial distress, including the restructuring of firms. Leasing companies are a particular efficient way of minimizing the costs of financial distress. If the firm misses payments, the leasing company simply repossesses the asset. Based on work by Cassar and Holmes (2003), Michaelas and Chittenden (1999), and Daskalakis and Psillaki (2008), some observations can be made about the demand for debt versus equity for SMEs. First, different types of loans and/or institutions finance different types of assets, and secondly, a single external source or type of funding is rarely sufficient to fund most projects. Thus, Iturralde, Maseda, and San-Jose (2010) suggest that Spanish SMEs benefit from multiple bank relationships and multiple funding sources. Garcia-Teruel

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and Martinez-Solano (2010) argue the debt maturity structure for SMEs conveys information to lenders— meaning that debt is not homogeneous. Similarly, Scherr and Hulbert (2001) and Aivazian, Ge and Qui (2005) show the maturity of assets affects the maturity of liabilities. This finding shows the two sides of the balance sheet are not independent.

Most of the capital structure literature has focused on homogeneous debt and the general debt-equity trade-off. An exception is Bolton and Freixas (2000) who examine the choice between bonds, bank loans, and equity. In addition, Berger and Udell (1998) have shown that different capital structures are optimal during different stages in the growth cycle of a firm. Taking this argument a step further, we suggest that different capital structures are optimal for funding different assets, even at a given point in time. Some funding sources are better for financing certain assets and each financing source may have its own collateral (which may only be the future earnings of the firm).

The finance literature somewhat recognizes that financing for SMEs is different from financing for large firms and that debt is not homogeneous. Our hypothesis of asset and liability matching builds most specifically upon two pieces of research. First, Faulkender and Peterson (2005) have shown the importance of the supply, or the availability of debt, in determining firms a firm’s capital structure. Second, Stowe, Watson, and Robertson (1980) have specifically stated that the asset and liability sides of the balance are interrelated for most firms. This lack of independence means the investing decision is not separate from the financing decision for most assets or projects.

DATA AND METHODOLOGY

The primary data source for this study is the Bank of Portugal Statistical Departments database. This database contains balance sheet and income statement data on 1,811 non-listed firms with 11,359 non- continuous firm year observations. We imposed several selection criteria to obtain a more homogeneous and usable sample. Only manufacturing firms for the period 1990-2000 with more than 100 employees for at least one year are included. This restriction minimizes the number of cases where the owner or the owner’s family uses their personal wealth to guarantee loans of the firm. Firms with negative net worth and less than three continuous data years are not included in the sample. We also deleted companies with observations lying in either tail (0.5%) of the distribution.

The final sample consists of 1416 firms and 7546 firm year observations. As shown in Table 1, 271 firms have data for the entire sample and about 200 firms have data for one or two years only. The analysis that follows uses 10 years of data because one year is lost in calculating changes in assets and liabilities. Around 100 firms have consecutive data for 4 to 9 years. Thus, the dataset over weights firms with only a few years of observations and firms with data for the entire 10-year period. The Portuguese government collected this SME data annually during the 1990s, but unfortunately quit collecting such detailed data after the year 2000.

As shown in Table 2, the data include six industry groups: Food and drinks, Textiles and clothes, Wood and paper past, Chemical products, Heavy industry, and Machinery and equipment. The total number of observations varies between 699 and 798 per year and each industry group includes a similar number of firms each year. An examination of the Bank of Portugal Statistical Department’s database indicates that our sample is representative of the structure of the Portuguese economy. Looking at the distribution of observations across industries, “Textiles and clothes” includes about a third of the total observations, whereas “Heavy industry” and “Wood and paper paste” each only contain about 15% of total observations.

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Table 1: Number of Firms with Consecutive Years of Data

Consecutive Years of Data Number of Firms 1 196 2 200 3 149 4 123 5 108 6 100 7 90 8 90 9 89 10 271 Total 1416 The table shows the number of firms in the sample and the number of years for which they have consecutive annual data. For example, only 271 of the 1416 total firms included in the sample have data for all 10 years. The sample is an unbalanced panel because many companies have less than 10 years of data.

Table 2: Number of Observations by Years and Industry

Year Industry Total Food And Textiles And Wood And Chemical Heavy Machinery Drinks Clothes Paper Paste Products Industry and Equipment 1991 102 236 56 125 53 127 699 1992 114 278 61 119 49 139 760 1993 107 272 63 121 48 128 739 1994 105 274 59 120 50 133 741 1995 109 274 67 130 51 137 768 1996 108 270 71 130 51 134 764 1997 106 272 70 132 56 128 764 1998 113 282 67 133 63 140 798 1999 111 277 70 133 61 138 790 2000 97 232 59 133 65 137 723 Total 1072 2667 643 1276 547 1341 7546 This table shows the number of observations in the sample for each industry for each year from 1991 to 2000. Annual totals across the six industries are shown in the right-most column. The bottom row shows the total number of observations for each industry for the entire 10-year period.

Sources and Uses of Funds for Portuguese Firms

In Table 3, the owners of the Portuguese firms provide nearly half (46-49%) of their firm’s required capital as equity. The common size balance sheet in Tables 4 and 5 show that Portuguese firms have close to 50% equity, which is similar to levels reported by Berger and Udell (1998) for SMEs in the US. In contrast, Rajan and Zingales (1995) report that large listed firms in the G7 countries have equity percentages ranging from 28% in Germany to 42% in the UK.

After internal equity, our data categorizes the liabilities on the balance sheet by three sources of external funding: (1) Other firms (Trade Credits), (2) Banks, and (3) Other Institutions and Miscellaneous providers of finance (e.g., leasing). Trade credits from suppliers constitute from 10% to 14% of SME funds. This compares to 26% for French, 22% for Spanish, and 11% for Swedish manufacturing SMEs, as reported by Garcia-Teruel and Martinez-Solano (2010) over the period 1996-2002. Banks provide 16- 20% of SME funds, and as shown in Table 4, about half of the bank loans are long-term and half are short-term. Most firms use a variety of sources of debt—meaning the debt-equity trade-off involves non- homogeneous debt.

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Table 3: Sources of Funds for Portuguese (Industrial) SMEs

1990 1994 1998 2000 % of total funds provided by Equity 49 46 49 46 Creditors (trade credit) 10 12 12 14 Banks 20 19 16 18 Other institutions and miscellaneous providers of credit 15 16 15 14 Provisions and accrued expenses 6 7 8 8

This table indicates the % of Portuguese SME funds provided in four different representative years by five broad categories of financing sources.

Other institutions (including leasing and factoring) account for 14 -16% of funds, while the remaining 6% to 8% of funding comes from provisions and accrued expenses. Funds from this last category eventually probably belong to one of the preceding categories of equity or debt. For example, provisions probably are part of internal equity. Accrued expenses are short-term liabilities recognized currently for expenses that will occur next year (e.g., vacation subsidies, social expenses, and rent). Any funding source can pay such expenses.

Table 4: Average Liabilities and Equity of Portuguese (Industrial) SMEs

1990 1992 1994 1996 1998 2000

Shareholder’s Funds 0.49 0.47 0.46 0.48 0.49 0.46 Capital 0.22 0.21 0.25 0.25 0.24 0.20 Reserves 0.23 0.25 0.19 0.21 0.22 0.21 Net Income of the Year 0.04 0.01 0.02 0.02 0.03 0.05 Provisions 0.02 0.01 0.01 0.01 0.01 0.01 Liabilities 0.49 0.52 0.53 0.51 0.50 0.53 Non-Current Liabilities 0.16 0.15 0.14 0.15 0.13 0.13 Long-Term Debt 0.13 0.12 0.09 0.11 0.09 0.10 Bank Loans 0.10 0.10 0.07 0.09 0.08 0.09 Other 0.03 0.02 0.02 0.02 0.01 0.01 Other Non-Current 0.03 0.03 0.05 0.04 0.04 0.03 Liabilities Current Liabilities 0.33 0.37 0.39 0.36 0.37 0.40 Loans 0.10 0.13 0.12 0.08 0.08 0.09 Bank Loans 0.10 0.13 0.10 0.08 0.08 0.09 Others <0.01 <0.01 <0.01 <0.01 <0.01 <0.00 Creditors 0.10 0.10 0.12 0.12 0.12 0.14 Other Current 0.09 0.09 0.09 0.09 0.10 0.10 Liabilities1 Accrued Expenses 0.04 0.05 0.06 0.07 0.07 0.07 The reported values are the fraction of shareholder funds and liabilities as a portion of total assets. They represent the right-hand side of an average common size balance sheet for the 1416 Portuguese SMEs. The bold-faced numbers are aggregate percentages for the three broadest categories—internal equity, provisions, and liabilities. Shareholder funds (internal equity) include 3 components: capital reserves, and net income of the year. Liabilities are the sum of non-current and current liabilities. Non-current liabilities are the sum of long-term debt and other non-current liabilities. Long-term debt consists of both bank loans and other loans. Similarly, current liabilities are the sum of short-term loans, creditors, other current liabilities, and accrued expenses.

In Table 4, current liabilities for Portuguese SMEs range from 33% to 40% of assets. For G7 countries, it ranges from 23% for Canada to 43% for France. Bank loans are the largest component of current liabilities (between 8% and 13% of assets). Trade credits total between 8% and 14% of total assets, while other liabilities represent about 10%. Banks provide 9% to 13% of common-size long-term debt. Overall banks and financial institutions account for 20 to 25% of SME financing in terms of loans. This is virtually identical to the 25% figure presented by Berger and Udell (1998) for US small business financing. Similarly, trade credits of 10% to 14% of funding are only slightly smaller than the 15% number reported by Berger and Udell (1998) for the US. Overall, SMEs in Portugal are financed much like SMEs in the US, but different from large listed firms in the G7.

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The discussion that follows lists the individual sources and uses of funds for Portuguese SMEs. It provides a rationale for our expectations for our expectations of which funding sources or sources should fund specific asset classes.

Sources of funds

1. Internal equity. Owners of SMEs often work in the company and they can generate more equity by drawing less salary and/or reducing dividend payments. The first role of equity for SMEs is the same as for larger companies--reducing the probability of default. For SMEs, equity also helps solve the asymmetric information problem encountered in debt financing. Since owners usually work in the company, they send a strong positive signal to lenders if they are willing to forego investment in other assets to invest in the company. Internal equity should be the primary funding source for assets with the most asymmetric information--intangible assets. In descending order of importance, internal equity would then be used for tangible assets, and then for working capital.

2. Cheap Trade credits. Trade credits represent financial services provided by other firms in competition with financial intermediaries. In a simple trade credit contract, the firm gets a discount if it pays on time. For example, perhaps a firm receives a 2% discount if it pays within 30 days. It gets no discount if it pays after 30 days. We categorize on-time payments as Cheap Trade Credits and late payments as Expensive Trade Credits. Smith (1987) and Petersen and Rajan (1997) have identified two broad motives for trade credits. The strategic motive of trade credits is that they are a signal that helps solve the asymmetric information problem regarding the firm’s products. They permit the buyer to verify the quantity and quality of a firm’s products before submitting payments. Trade credits also help establish long-term relationships between suppliers and buyers. The financial motive for trade credits is that firms compete with financial institutions in offering credit to other firms. Petersen and Rajan (1997) argue that suppliers have a closer relationship with the producing firm than the bank. These firms may know more about a firm’s ability to pay than a bank. The use of early payment discounts provides the supplier with an indication of credit worthiness.

The supplier may have advantages over financial institutions in collecting payments. If the supplier has a local monopoly for the goods, it can to withhold future deliveries to encourage payment. In case of default, the supplier can take back the goods and resell them easier than a financial intermediary reclaiming the same goods. Due to supplier’s general knowledge of the firm and the industry, the level of asymmetric information is relatively low between the providers of trade credits and the borrowers. It is an efficient source of funding for current assets, such as inventories. However, if a firm overdraws these credits, trade credits can become expensive.

3. Expensive trade credits. Delayed payment of trade credits is expensive because it involves giving up the discount and it may incur penalty payments. Use of expensive trade credits affects reputation and it may reduce access to future trade credits. Overdrawn trade credits also send a signal to the bank that may increase the cost of bank financing. Firms should avoid expensive trade credits and treat them as a financing source of last resort.

4. Bank loans. Banks have information about the general financial health of the firm whereas providers of trade credit have specific information about the conditions in the industry and the general competitive position of the firm. Banks collects the information through due diligence and through the transactions accounts of the firm. Although providers of trade credits have an advantage over banks in assessing the value of the collateral they have themselves delivered, banks have an advantage in selling general collateral such as buildings, machinery etc. Banks

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therefore prefer to issue loans using tangible assets as collateral. Due to asymmetric information, small firms and high growth firms may have to provide considerable internal equity to convince the bank to extend loans for some tangible assets. Overall, however, banks are likely to be a major provider of capital for the purchase of tangible assets for SMEs. Short-term bank loans serve different purposes. The first is the simple provision of liquidity, e.g. to bridge seasonality in payments. The second is financing accounts receivable, translated as debtors on Portuguese balance sheets. Banks have a comparative advantage in evaluating the creditworthiness of the debtors, because for a large bank several of the debtors may be bank customers. Short-term bank loans issued in conjunction with long-term bank loans to finance tangible assets provide the bank with an easy option to stop unprofitable projects. Thus, short-term bank loans may be used for a variety of purposes—short-term liquidity, and to finance debtors, intangible assets, and tangible assets.

5. Other non-current liabilities (leasing). The balance sheets of Portuguese SMEs (Table 4) contain two items for miscellaneous long- term debt labeled “Long-Term Debt Other” and “Other Non- Current Liabilities”. “Long-term Debt Other” may contain car or equipment loans, which are more expensive than bank loans. “Other Non-Current Liabilities” contains, among other items, leasing contracts and factoring. Leasing is an efficient way of resolving financial distress. When a firm misses a payment, the lessor can simply retrieve the asset. “Other Non-Current Liabilities” should primarily finance Tangible Assets.

6. Other short-term debt. This category of short-term liabilities includes “Others” and “Other Current Liabilities”. Factoring, or the sale of receivables for immediate funds, is included in “Other Current Liabilities”. However, the financial statements provide little information about the content of these two accounts.

Uses of Funds

Table 5 indicates the uses of funds. Tangible assets comprise about 40% of total assets, intangible assets represent 2%, and investments are 10% of total assets. About 50% of firm assets are current assets— primarily consisting of debtors (about 25%) and inventories (about 15%). Cash represents 3%-4% of total assets, while stocks (liquidity) and prepaid expenses together comprise the remaining 2% of total assets.

Table 5: Average Assets of Portuguese (Industrial) SMEs

1990 1992 1994 1996 1998 2000 Assets Fixed Assets 0.52 0.53 0.54 0.50 0.51 0.47 Intangible Assets 0.01 0.01 0.04 0.04 0.03 0.02 Tangible Assets 0.43 0.42 0.40 0.37 0.39 0.34 Investments 0.08 0.10 0.10 0.09 0.09 0.11 Current Assets 0.48 0.47 0.46 0.50 0.49 0.52 Stocks (Liquidity) 0.02 0.01 0.01 0.02 0.01 0.01 Debtors 0.24 0.24 0.26 0.29 0.26 0.30 Inventories 0.19 0.17 0.15 0.14 0.15 0.16 Cash and cash 0.02 0.03 0.03 0.04 0.06 0.04 Equivalents Prepaid Expenses 0.01 0.02 0.01 0.01 0.01 0.01 The numbers represent the left-hand side of an average common size balance sheet for the 1416 Portuguese SMEs. The reported values are the fraction of various assets as a portion of total assets for six representative years. It indicates the uses of SME funds. Fixed assets are the sum of intangible assets, tangible assets, and investments. Current asset are the sum of stocks (liquidity), debtors (accounts receivable), inventories, cash and cash equivalents, and prepaid expenses

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1. Intangible assets. To reiterate the discussion above, intangible assets are associated with considerable asymmetric information. Therefore, we expect internal equity to be the primary funding source and bank loans to be the secondary funding source.

2. Tangible assets. Tangible assets have less asymmetric information and they are better collateral than intangible assets. Because of the important signal conveyed by using internal equity, it usually finances a portion of tangible assets. Bank loans are the main funding source for most tangible assets. Banks issue mainly long-term loans, but combine these with some short-term bank loans to solve the moral hazard problem. Leasing, which is included in “Other Non-Current Liabilities” is a secondary avenue for financing tangible assets.

3. Investments. This category includes real estate, stocks, bonds, and investments in subsidiaries. Long-term investments have various degrees of asymmetric information and collateral value. Internal equity and long-term bank debt are probably the primary sources for this type of financing.

4. Liquid assets. These assets include cash and cash equivalents, inventories, debtors (accounts receivable), stocks (liquidity), and prepaid expenses. Most of these assets have very little asymmetric information and should funded primarily by short-term and somewhat by long-term bank loans. The data on inventories does not distinguish between own produced goods and goods purchased from other suppliers. Since Cheap Trade Credits are quite efficient, they should finance goods and services purchased from other firms. For own produced goods, the asymmetric information problem likely requires some combination of internal equity and short- term bank loans. For debtors (accounts receivable), banks have comparative advantages in assessing credit. Short-term bank loans probably fund these assets, but some internal equity might be necessary if there is considerable uncertainty about making payments. If the firm uses factoring, then Other Short Term Debt becomes an important of financing for debtors. Firms will use Expensive Trade Credits only they cannot use other lower cost and preferred sources.

In the discussion above, we have hypothesized that each asset has its own primary and secondary sources of funding and that different assets have different capital structures. In the pecking order theory of Myers and Majluf (1984), firms add up external funding needs and then choose the cheapest funding source first, regardless of the use of the funds. They exhaust this source and move to the next one. Our theory does not preclude pecking order financing, but instead suggests there may be a separate pecking order for each type of asset. The other popular theory about capital structure is the static trade-off theory. In this theory, the cost of financing depends on expected bankruptcy costs and agency problems. Firms choose financing so that the marginal cost of each source is equal. At equilibrium, a firm is indifferent between borrowing a new Euro of funds from any of the financing sources. As with the pecking order theory, this theory could be consistent with our model if each type of asset has its own static trade-off representing the relative costs of bankruptcy, asymmetric information, and agency problems.

Classifying Trade Credits

Table 1 indicates that Trade Credits are an important source of funds—constituting 10% to 14% of all funds provided to Portuguese SMEs. A standard textbook trade credit contract quoted is 2-10 net 30. The contract has a discount rate of 2% if the customer pays the bill within 10 days. Otherwise, the full amount is due in 30 days. The contracts in Portugal are simpler than standard contracts. A quote of 2 net 30, for example, means the customer receives the full 2% discount for paying the bill within 30 days. The customer forgoes the discount and often pays a penalty on payments after the due date. Eurofactor (2006) reported that 22% of Portuguese companies imposed late payment charges in 2005 and that 93% of these companies actually collected late payment penalties.

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At some point after the due date, the firm extending credit may start legal proceedings to collect the debt. According to Eurofactor (2006), the average payment period for trade credits in Portugal was 53 days in 2003. This time frame is roughly equivalent to the UK, based on Poutziouris, Michaelas and Soufani 2005). They also found that the average late payment was 45 days beyond the due date. In Portugal, 88% of the companies start the debt recovery process after an average late period of 42 days. Thus, it appears that the threat of starting debt recovery process encourages rather quick payment on late accounts.

In theory, the definitions of cheap and expensive trade credits are straightforward. If the number of credit days is larger than specified in the contract, then the trade credits are expensive. Payments made before the due date are cheap trade credits. Unfortunately, the balance sheet does not provide information about the cost of trade credits, nor the terms of the contracts. We propose to distinguish between cheap and expensive trade credits by estimating three numbers. These are the current “age” of Trade Credits on the balance sheet, the terms (number of trade credit contracts in the industry), and the standard deviation of the number of credits days for in each industry. The Appendix provides the details concerning these calculations.

We calculate the number of credit days for firms shown in Figure 1 as the value of trade credits divided by the cost of goods sold divided by 365. Since the distribution of credit days is right-skewed, we use the most common number of credit days (mode) for each industry as a point estimate of the number of credit days in the trade credit contract for that industry. The numbers reported contain random fluctuations, so we need to calculate the standard deviation (σ) of credit days. We use the left-side semi-variance to calculate the variance of the distribution of credit days for each industry. If the “age” of the Trade Credits reported on the balance sheet is greater than estimated contract terms, or credit days plus an amount added to account for uncertainty in reporting, then the trade credits are Expensive. Otherwise, trade credits are Cheap. To summarize, the distinction between Expensive and Cheap Trade Credits is:

Cheap Trade Credits: If actual credit days < contract credit days +1.96σ (1)

Expensive Trade Credits: If actual credit days > contract credit days +1.96σ (2)

Figure 1: Credit days for sample of Portuguese SMEs

Credit days for entire sample 600

500 400 300 200 100 Number of of Number firms 0 0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200 210 220 230 240 250 260 270 280 290 300 310 320 330 340

Number of credit days This figure shows the frequency distribution of credit days used among the 9187 firm-years of data. The mode is between 75 and 85 days meaning that in 571 of the 9187 cases, firms pay their credits between 75 and 85 days after receiving trade credit. The average number of days of trade credit is 106 days and the median is 92 days.

Table 6 indicates that there is a large variation in the use of expensive trade credits across industries. In the Machinery and Equipment industry, only 8% of the firms make use of expensive credits. For the Food and Drink, and Heavy Machinery industries, about 47% of the firms use expensive credits. A survey by

J. Bartholdy et al | IJBFR ♦ Vol. 6 ♦ No. 4 ♦ 2012

Howorth and Reber (2003) indicates that 57% of SMEs in the UK occasionally pay their creditors late, while Ng, Smith and Smith (1999) report that 30% of US firms do not claim the trade credit discount. Our estimates for Portugal are below those for the UK, but generally in line with survey evidence about the prevalence of cheap versus expensive trade credits.

Table 6: Distribution of Expensive Trade Credits

Industry Number of Firms % of Firms with Expensive Credits as a Expensive Credit % of Total Credit

Food and drinks 818 47.07 63.58 Textiles and clothes 1913 27.76 38.89 Wood and paper paste 481 16.01 19.14 Chemical products 956 33.62 32.87 Heavy machinery 383 47.26 41.45 Machinery production and equipment 954 8.07 5.97 This table shows the total number of firms in each industry, the percentage of those firms that we estimate that are using expensive trade credits, and the percentage of all trade credit in an industry that represents expensive trade credit. The appendix presents the details for making these calculations.

EMPIRICAL RESULTS

The Simultaneous Equation Model

As previously discussed, the eight sources of funds are internal equity (EQ), cheap trade credits, expensive trade credits (ETC), long-term bank loans (LTB), short-term bank loans STB), other short-term loans from non-bank financial institutions (OST), other non-current credits (ONC) , and long-term debt (other) (LDO). Funding requirements on the asset side of the balance sheet for the six asset classes therefore determine the annual change in each of the eight sources of funds. The asset classes are intangible assets (Intan), tangible assets (Tan), investments (Inv), and changes in working capital. Working capital is the sum of liquid assets (Liquid), accounts receivable (Debtors), and inventories (Inven). The economic intuition behind the system is that the firm generates the projects requiring financing and then approaches the financial institutions for funding. Causation is from projects or assets to financing. To test this hypothesis, we setup a simultaneous system of equations, as shown in equations (3) – (10).

= + + + + + + 퐸푄 + 퐸푄 퐸푄 퐸푄 퐸푄 퐸푄 푖푡 1 푖푡 2 푖푡 3 푖푡 4 푖푡 5 푖푡 (3) ∆퐸푄퐸푄 훼 훽 훥퐼푛푣 훽 퐼푛푡푎푛 훽 푇푎푛 훽 퐿푖푞푢푖푑 훽 퐷푒푏푡표푟푠 6 푖푡 푖푡 훽 퐼푛푣푒푛= +휀 + + + + + 퐶푇퐶 + 퐶푇퐶 퐶푇퐶 퐶푇퐶 퐶푇퐶 퐶푇퐶 (4) 푖푡 1 푖푡 2 푖푡 3 푖푡 4 푖푡 5 푖푡 ∆ 퐶푇퐶퐶푇퐶 훼 훽 훥퐼푛푣 훽 퐼푛푡푎푛 훽 푇푎푛 훽 퐿푖푞푢푖푑 훽 퐷푒푏푡표푟푠 6 푖푡 푖푡 훽 퐼푛푣푒푛= +휀 + + + + + 푆푇퐵+ 푆푇퐵 푆푇퐵 푆푇퐵 푆푇퐵 푆푇퐵 (5) 푖푡 1 푖푡 2 푖푡 3 푖푡 4 푖푡 5 푖푡 ∆푆푇퐵푆푇퐵 훼 훽 훥퐼푛푣 훽 퐼푛푡푎푛 훽 푇푎푛 훽 퐿푖푞푢푖푑 훽 퐷푒푏푡표푟푠 6 푖푡 푖푡 훽 퐼푛푣푒푛= +휀 + + + + + 푂푆푇+ 푂푆푇 푂푆푇 푂푆푇 푂푆푇 푂푆푇 (6) 푖푡 1 푖푡 2 푖푡 3 푖푡 4 푖푡 5 푖푡 ∆ 푂푆푇푂푆푇 훼 훽 훥퐼푛푣 훽 퐼푛푡푎푛 훽 푇푎푛 훽 퐿푖푞푢푖푑 훽 퐷푒푏푡표푟푠 6 푖푡 푖푡 훽 퐼푛푣푒푛= +휀 + + + + + 퐸푇퐶+ 퐸푇퐶 퐸푇퐶 퐸푇퐶 퐸푇퐶 퐸푇퐶 (7) 푖푡 1 푖푡 2 푖푡 3 푖푡 4 푖푡 5 푖푡 ∆ 퐸푇퐶퐸푇퐶 훼 훽 훥퐼푛푣 훽 퐼푛푡푎푛 훽 푇푎푛 훽 퐿푖푞푢푖푑 훽 퐷푒푏푡표푟푠 6 푖푡 푖푡 훽 퐼푛푣푒푛= +휀 + + + + + 퐿푇퐵+ 퐿푇퐵 퐿푇퐵 퐿푇퐵 퐿푇퐵 퐿푇퐵 (8) 푖푡 1 푖푡 2 푖푡 3 푖푡 4 푖푡 5 푖푡 ∆퐿푇퐵퐿푇퐵 훼 훽 훥퐼푛푣 훽 퐼푛푡푎푛 훽 푇푎푛 훽 퐿푖푞푢푖푑 훽 퐷푒푏푡표푟푠 6 푖푡 푖푡 훽 퐼푛푣푒푛= +휀 + + + + 푂푁퐶 푂푁퐶 푂푁퐶 푂푁퐶 푂푁퐶 푂푁퐶 ∆푂푁퐶푖푡 훼 훽1 훥퐼푛푣푖푡 훽2 퐼푛푡푎푛푖푡 훽3 푇푎푛푖푡 훽4 퐿푖푞푢푖푑푖푡 훽5 퐷푒푏푡표푟푠푖푡 22

The International Journal of Business and Finance Research ♦ VOLUME 6 ♦ NUMBER 4 ♦ 2012

+ + (9) 푂푁퐶 6 푖푡 푖푡 훽 퐼푛푣푒푛= +휀 + + + + + + 퐿퐷푂 퐿퐷푂 퐿퐷푂 퐿퐷푂 퐿퐷푂 퐿퐷푂 (10)퐿퐷푂 푖푡 1 푖푡 2 푖푡 3 푖푡 4 푖푡 5 푖푡 6 푖푡 ∆ 퐿퐷푂 훼 훽 훥퐼푛푣 훽 퐼푛푡푎푛 훽 푇푎푛 훽 퐿푖푞푢푖푑 훽 퐷푒푏푡표푟푠 훽 퐼푛푣푒푛 푖푡 The휀 changes in assets requiring funding are the dependent or right-hand side variables. The changes in the various types of liabilities are the left-hand side, or independent variables. The notation Δ denotes change in, and the names of the funding sources (EQ, CTC, STB, OST, ETC, LTB, ONC, and LDO) and asset classes (Inv, Intan, Tan, Liquid, Debtors, and Inven) are as presented above. We estimate this system of equations using seemingly unrelated regression with the equation for prepaid expenses and provisions left out of the estimation procedure. The various α coefficients are the intercept coefficients for each funding source equation. Each β coefficient represents the estimated change in a funding source required (in Euros) for a one Euro change in the use of the asset considered. Alternatively, the coefficients are the percentage of funds for a given asset on the right-hand side of the equation that comes from the funding source on the left-hand side of the equation.

Tests for Independence

The first question examined is the independence of the asset and liability sides of the balance sheet. There are two different ways of looking at independence. First, if the financing of an asset is independent of the type of asset in question, then an increase in any asset (e.g. tangible assets) should have the same impact on a funding source (e.g. long-term bank debt) as an increase in another asset (e.g. intangible assets). The hypothesis becomes a within equation test of the following linear restrictions applied to each of the eight individual sources of funds, such that:

= = = = = , = 1, … 8 ( , , , , , , , ) (11) 푗 푗 푗 푗 푗 푗 The훽1 results훽2 훽 in3 Table훽4 7훽 indicate5 훽6 푓표푟that 푗the β coefficients퐸푄 퐶푇퐶 are푆푇퐵 not푂푆푇 identical퐸푇퐶 퐿푇퐵 in each푂푁퐶 of푎푛푑 the eight퐿퐷푂 equations. Such results reject this definition of independence for each source of debt. The second way of describing independence is by using the static-trade-off model. At equilibrium, if the model holds, the marginal cost of one Euro of any of the six asset classes should be equal for each of the eight sources of funds. For example, at equilibrium, each of the eight sources of funds equally funds a one Euro investment in tangible assets. For the system of equations, the static trade-off model is a test of the cross equation restrictions on each type of asset as follows:

======, 퐸푄 = 1퐶푇, …퐶 6 ( 푆푇퐵, 푂푆푇, , 퐸푇퐶 , 퐿푇퐵 푂푁퐶, )퐶푇퐶 퐶푇퐶 푖 푖 푖 푖 푖 푖 푖 푖 푖 (12) 훽 훽 훽 훽 훽 훽 훽 훽 훽 Again,푓표푟 푖 from Table퐼푛푣 퐼푛푡푎푛7, we 푇푎푛reject퐿푖푞푢푖푑 these퐷푒푏푡표푟푠 restrictions퐼푛푣푒푛 for each type of asset (as well as jointly across all the assets). Thus, we reject both independence and static-trade-off theory for our sample of Portuguese SMEs. The second question to examine is what constitutes debt in the capital structure decision. The main concern is whether short-term debt is part of the capital structure decision, or if it is only part of working capital. Reflecting the lack of guidance from the theoretical literature on capital structure, many researchers only consider long-term debt in the capital structure. Some also include a portion of short- term debt, while others incorporate all forms of short-term and long-term debt into their measurement of debt. The broadest measure of the debt ratio is total liabilities divided by total liabilities plus net worth, as in Rajan and Zingales (1995) and Booth et al (2001).

Under a narrow interpretation of debt, cheap trade credits, short-term bank loans, other short debt, and expensive trade credits should only finance working capital. In our framework, the following restrictions represent this narrow interpretation of debt:

J. Bartholdy et al | IJBFR ♦ Vol. 6 ♦ No. 4 ♦ 2012

======0,퐶푇퐶 퐶푇퐶1, 2, 3 =퐶푇퐶 , 푆푇퐵 , 푆푇퐵 푆푇퐵 퐶푇퐶 푂푆푇 푂푆푇 퐸푇퐶 퐸푇퐶 퐸푇퐶 (13) 1 2 3 1 2 3 1 2 3 1 2 3 훽 훽 훽 훽 훽 훽 훽 훽 훽 훽 훽 훽 The푤ℎ restrictions푒푟푒 imply퐼푛푣 퐼푛푡푎푛 a coefficient푇푎푛 of zero for the impact of changes in the three components of working capital on the financing of Investments, Intangible assets and Tangible assets. If the restrictions hold, structure. Another possible working capital does not fund these assets and it is not part debt in the capital restriction is that long-term bank loans and other long-term debt finance only long-term assets (investments, intangible assets, and tangible assets), so that:

======0, 4, 5, 6 = 퐿푇퐵 , 퐿푇퐵 ,퐿푇퐵 푂푁퐶 푂푁퐶 푂푁퐶 퐿퐷푂 퐿퐷푂 퐿퐷푂 (14) 4 5 6 4 5 6 4 5 6 훽 훽 훽 훽 훽 훽 훽 훽 훽 푤ℎ푒푟푒 Results퐿푖푞푢푖푑 퐷푒푏푡표푟푠from the 퐼푛푣푒푛푡표푟푦tests presented in Table 7 strongly reject both the restrictions implied by equations (13 and (14). Thus, all types of debt are part of the capital structure decision. Empirical studies using debt ratios should adopt broad debt measures containing all types of short-term and long-term debt, as well as trade credits.

Table 7: Tests of Independence

Hypothesis Tests for Independence Between the Six Asset Classes and Liabilities: Chi-Squared Significance Level Cheap trade credits: = = = = = 61.06*** 0.000 Short-bank loans: 퐶푇퐶 = 퐶푇퐶 = 퐶푇퐶 = 퐶푇퐶 = 퐶푇퐶 = 퐶푇퐶 12.37*** 0.000 훽1 훽2 훽3 훽4 훽5 훽6 Other short term loans: 푆푇퐵 = 푆푇퐵 = 푆푇퐵 = 푆푇퐵 = 푆푇퐵 = 퐶푆푇퐵 26.14*** 0.000 훽1 훽2 훽3 훽4 훽5 훽6 Expensive trade credits: 푂푆푇 = 푂푆푇 = 푂푆푇 = 푂푆푇 = 푂푆푇 = 푂푆푇 16.07*** 0.000 1 2 3 4 5 6 Long term bank loans: 훽 퐸푇퐶 = 훽 퐸푇퐶 = 훽 퐸푇퐶 = 훽 퐸푇퐶 = 훽 퐸푇퐶 = 훽 퐸푇퐶 395.38*** 0.000 1 2 3 4 5 6 Other Non-Current-Liabilities: 훽퐿푇퐵 = 훽퐿푇퐵 =훽퐿푇퐵 =훽퐿푇퐵 =훽퐿푇퐵 =훽퐿푇퐵 154.03*** 0.000 1 2 3 4 5 6 Independence of long-term assets훽 and푂푁퐶 short훽 푂푁퐶-term funds:훽 푂푁퐶 훽 푂푁퐶 훽 푂푁퐶 훽 푂푁퐶 518.59*** 0.000 1 2 3 4 5 6 = = = = 훽 = 훽 = 훽 = 훽 = 훽 = 훽 = = = 0 퐶푇퐶, 퐶푇퐶 1, 2, 3 =퐶푇퐶 , 푆푇퐵 , 푆푇퐵 푆푇퐵 퐶푇퐶 푂푆푇 푂푆푇 퐸푇퐶 퐸푇퐶 퐸푇퐶 1 2 3 1 2 3 1 2 3 1 2 3 Independence훽 훽 of훽 short-term훽 assets훽 and long훽 -term 훽funds: 훽 훽 훽 훽 훽 2511.4*** 0.000 푤ℎ푒푟푒 = 퐼푛푣 퐼푛푡푎푛= 푇푎푛======0, 4, 5, 6퐿푇퐵= 퐿푇퐵, 퐿푇퐵 , 푂푁퐶 푂푁퐶 푂푁퐶 퐿퐷푂 퐿퐷푂 퐿퐷푂 4 5 6 4 5 6 4 5 6 This table shows훽 the훽 equations훽 used훽 to test 훽for independence훽 훽 between훽 asset훽 and liabilities. The chi-squared statistic tests reject the null hypotheses푤ℎ푒푟푒 implied 퐿푖푞푢푖푑by each퐷푒푏푡표푟푠 of the coefficient퐼푛푣푒푛푡표푟푦 restrictions at the 1% level of significance. Results therefore reject independence between short and long term funding sources. The notation ***, **, and * denoted significance at the 1, 5 and 10 percent levels, respectively. The funding sources are EQ = Internal Equity, CTC = cheap trade credits, LTB = long term bank loans, ONC = other non-current liabilities, STB = short term bank loans, OST = other short term bank loans, LDO = other long term debt, and ETC = expensive trade credits. For the right-hand side variables, Inv = 1= investments in long-term financial assets, Intan = 2 = intangible assets, Tan = 3 = Tangible Assets, Liquid = 4 = cash and liquid investments, Debtors = 5 = accounts receivable, and Inven = 6 = inventories. The system is estimated by SUR to facilitate cross-equation tests, with the equation for prepaid expenses left out. The model also contains five unreported industry dummies that effectively expand the intercept terms. The following model is estimated by Seemingly Unrelated Regressions (SUR). = + + + + + + + (3) = 퐸푄 +퐸푄 +퐸푄 +퐸푄 퐸푄+ 퐸푄+ 퐸푄+ + (4) 푖푡 1 푖푡 2 푖푡 3 푖푡 4 푖푡 5 푖푡 6 푖푡 푖푡 ∆퐸푄 =훼 퐶푇퐶 +훽 퐶푇퐶훥퐼푛푣 훽+ 퐼푛푡푎푛퐶푇퐶 훽+ 푇푎푛퐶푇퐶 훽 +퐿푖푞푢푖푑퐶푇퐶 훽 +퐷푒푏푡표푟푠퐶푇퐶 훽 +퐼푛푣푒푛퐶푇퐶 휀 + (5) 푖푡 1 푖푡 2 푖푡 3 푖푡 4 푖푡 5 푖푡 6 푖푡 푖푡 ∆퐶푇퐶 = 훼푆푇퐵 + 훽푆푇퐵 훥퐼푛푣 + 훽푆푇퐵 퐼푛푡푎푛 + 훽푆푇퐵 푇푎푛 + 훽푆푇퐵 퐿푖푞푢푖푑 + 훽푆푇퐵 퐷푒푏푡표푟푠 + 훽푆푇퐵 퐼푛푣푒푛 + 휀 (6) 푖푡 1 푖푡 2 푖푡 3 푖푡 4 푖푡 5 푖푡 6 푖푡 푖푡 ∆푆푇퐵 = 훼푂푆푇 + 훽푂푆푇 훥 퐼푛푣 +훽푂푆푇 퐼푛푡푎푛 +훽푂푆푇 푇푎푛 +훽푂푆푇 퐿푖푞푢푖푑 +훽푂푆푇 퐷푒푏푡표푟푠 +훽푂푆푇 퐼푛푣푒푛 +휀 (7) 푖푡 1 푖푡 2 푖푡 3 푖푡 4 푖푡 5 푖푡 6 푖푡 푖푡 ∆푂푆푇 = 훼 퐸푇퐶 + 훽 퐸푇퐶훥 퐼푛푣 +훽 퐸푇퐶퐼푛푡푎푛 +훽 퐸푇퐶푇푎푛 +훽 퐸푇퐶퐿푖푞푢푖푑 +훽 퐸푇퐶퐷푒푏푡표푟푠 +훽 퐸푇퐶퐼푛푣푒푛 +휀 (8) 푖푡 1 푖푡 2 푖푡 3 푖푡 4 푖푡 5 푖푡 6 푖푡 푖푡 ∆퐸푇퐶 =훼퐿푇퐵 +훽퐿푇퐵 훥 퐼푛푣 +훽퐿푇퐵 퐼푛푡푎푛 +훽퐿푇퐵 푇푎푛 +훽퐿푇퐵 퐿푖푞푢푖푑 +훽퐿푇퐵 퐷푒푏푡표푟푠 훽+퐿푇퐵 퐼푛푣푒푛 휀+ (9) 푖푡 1 푖푡 2 푖푡 3 푖푡 4 푖푡 5 푖푡 6 푖푡 푖푡 ∆퐿푇퐵 = 훼 푂푁퐶 +훽 푂푁퐶훥 퐼푛푣 +훽 푂푁퐶퐼푛푡푎푛 +훽 푂푁퐶푇푎푛 +훽 푂푁퐶퐿푖푞푢푖푑 +훽 푂푁퐶퐷푒푏푡표푟푠 +훽 푂푁퐶퐼푛푣푒푛 +휀 (10) 푖푡 1 푖푡 2 푖푡 3 푖푡 4 푖푡 5 푖푡 6 푖푡 푖푡 ∆푂푁퐶 훼퐿퐷푂 훽퐿퐷푂 훥퐼푛푣 훽퐿퐷푂 퐼푛푡푎푛 훽퐿퐷푂 푇푎푛 훽퐿퐷푂 퐿푖푞푢푖푑 훽퐿퐷푂 퐷푒푏푡표푟푠 훽퐿퐷푂 퐼푛푣푒푛 휀 푖푡 1 푖푡 2 푖푡 3 푖푡 4 푖푡 5 푖푡 6 푖푡 푖푡 ∆Funding퐿퐷푂 훼 for Individual훽 훥퐼푛푣 A훽ssets퐼푛푡푎푛 훽 푇푎푛 훽 퐿푖푞푢푖푑 훽 퐷푒푏푡표푟푠 훽 퐼푛푣푒푛 휀

Based on the theory of asymmetric information and financial distress in previous sections, we developed a set of predictions of financing sources for each individual asset class.

The International Journal of Business and Finance Research ♦ VOLUME 6 ♦ NUMBER 4 ♦ 2012

Investments in real estate, stocks, bonds, and subsidiaries have little asymmetric information. However, the reasons for the investments vary and the preferred financing source may depend on the type of investment. One might expect a variety of financing sources and Table 8 shows that all financing sources except trade credits fund additional investments. The two main financing sources are internal equity (18%) and long-term bank loans (61%).

Intangible assets have considerable asymmetric information and no collateral value. Internal equity should be the primary source of funding. In Table 8, a one Euro investment in intangible assets increases internal equity by 0.719. Coefficients for other funding sources are quite small and only marginally significant (at the 10% level) from other sources.

Tangible Assets have some asymmetric information. They have a collateral value, but there may be agency and moral hazard problems. We therefore expect a mixture of internal equity, long-term bank debt, short-term bank loans, other short-term loans, other non-current liabilities (leasing). A one Euro investment in tangible assets is financed by Euro 0.35 in internal equity, Euro 0.17 in long-term bank loans, Euro 0.13 in other non-current liabilities, and Euro 0.13 in short-term bank loans. However, it also appears that firms are somewhat constrained in their financing of tangible assets since Euro 0.1 comes from the most expensive type of financing--expensive trade credits.

Financing for Liquid assets comes primarily from internal equity (39%) and short-term other loans (15%). An unexpected result is that Expensive Trade Credits finance 8% of additional liquid assets. It is unclear why any firm would use such expensive financing for liquid assets. Notice also that 19% of the financing for liquid assets comes from various long-term financing sources. This result rejects the notion that we can separate the financing of working capital from the long-term sources of debt.

The primary source of financing for Debtors (accounts receivable) is other non-current liabilities (36%), which may be from long-term factoring contracts. Other important funding sources include internal equity (17%) and short-term bank loans (11%). This funding mix is about what we expected. Cheap trade credits provide 7% of funding, while expensive trade credits contribute 13%. Thus, it appears that Portuguese firms may extend their own trade credits to finance their own customers.

Inventories, both finished goods and supplies, obtain 14% of their financing from cheap trade credits and 20% from expensive trade credits. The other main funding sources are short-term bank loans (18%) and short-term other loans (23%). Long-term financing sources play only a minor role--supporting the standard practice of separating the financing of working capital and long-term assets.

Our empirical results generally confirm the financing predictions from section 4 concerning the sources and uses of funds for Portuguese SMEs. Each asset appears to have its own capital structure and the weighted average cost of capital may vary on a project-by-project basis. All debt is important in the capital structure and different assets will incur different financing costs.

CONCLUSION

This paper has demonstrated that the asset and liability sides of the balance sheet are interrelated for a sample of small and medium-sized Portuguese firms. Tests of independence reject the independence between the two sides of the balance sheet—a result that is consistent with our theory that firms match specific assets with a specific set of liabilities. We have shown that firms finance long-term assets using both short-term and long-term debt and that all types of debt (trade credits, bank loans, leasing, non-bank loans, and other debt) are part of the capital structure decision. Thus, empirical studies should use broad debt measures in capital structure calculations and recognize that there may not be a unique weighted

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average cost of capital. Instead, each asset or project may have a different weighted average cost of capital.

In this study, we have examined only small and medium-sized Portuguese firms for the years 1990-2000. The introduction of the Euro in 1999 has increased the competition in the financial sector in Europe. Therefore, if data were available, and it would be interesting to see whether the behavior of Portuguese SMEs has changed. That is, we would like to perform tests of independence between the two sides of the balance sheet for a more recent period. More importantly, we would like to extend our analysis to an international sample of both large and small firms. Such a sample would permit a better test of our theory of asset and liability matching and help to determine whether broad measures of debt are more accurate than the narrow definition used in most of the capital structure literature.

Table 8: Testing for Factors Driving the Change in Financing Sources

Changes in Variables EQ CTC STB OST ETC ONC LDO LTB Constant 0.011*** -0.001 0.005* 0.000 -0.000 -0.003 0.012*** 0.002*** (4.08) (-0.32) (1.83) (0.05) (-0.06) (-1.38) (-4.23) (-2.59) Intangible 0.719*** 0.013 0.007 0.026 0.076* 0.064* 0.034 0.016 assets (20.20) (0.37) (0.20) (0.71) (1.72) (1.92) (0.89) (1.74) Tangible 0.350*** 0.015 0.128*** 0.067*** 0.100*** 0.169*** 0.128*** 0.006** assets (32.39) (1.40) (11.92) (6.09) (7.45) (16.64) (11.12) (2.01) Investments 0.183*** 0.015 0.065*** 0.041*** 0.0121 0.613*** 0.062*** 0.009*** (14.71) (1.18) (5.30) (3.24) (0.78) (52.32) (4.63) (2.79) Liquid assets 0.388*** 0.065*** 0.060*** 0.151*** 0.081*** 0.054*** 0.136*** 0.004 (24.38)*** (4.02) (3.83) (9.38) (4.07) (3.59) (8.01) (0.92) Debtors 0.173*** 0.069*** 0.110*** 0.088*** 0.135*** 0.034*** 0.364*** 0.001 (23.96) (9.36) (15.32) (12.07) (14.98) (4.95) (47.18) (0.53) Inventories 0.106*** 0.140*** 0.177*** 0.2333*** 0.203*** 0.052*** 0.034** -0.000 (7.56) (9.81) (12.76) (16.40) (11.58) (3.93) (2.24) (-0.10) “R-Squared” 0.2714 0.0265 0.0731 0.0671 0.0556 0.2880 0.2413 0.0032 This table presents the coefficient estimates for equations (3) – (10). However, the entries have been transposed from the normal representation between rows and columns to provide a more intuitive explanation of how each asset is funded. For example, for a one Euro increase in Intangible assets, .719 comes from internal equity, .013 from cheap trade credits, .007 from short-term bank loans, .007 from other short-term loans, .076 from expensive trade credits, .064 from long-term bank loans, .034 from other non-current liabilities, and .01 from other long-term debt. Sources of funding for each of the other five asset classes can be similarly read by going across each row in the table. The notation ***, **, and * denoted significance at the 1, 5 and 10 percent levels, respectively. The funding sources are EQ = Internal Equity, CTC = cheap trade credits, LTB = long term bank loans, ONC = other non-current liabilities, STB = short term bank loans, OST = other short term bank loans, LDO = other long term debt, and ETC = expensive trade credits. For the right-hand side variables, Inv = 1= investments in long-term financial assets, Intan = 2 = intangible assets, Tan = 3 = Tangible Assets, Liquid = 4 = cash and liquid investments, Debtors = 5 = accounts receivable, and Inven = 6 = inventories. The system is estimated by SUR to facilitate cross-equation tests, with the equation for prepaid expenses left out. The model also contains five unreported industry dummies that effectively expand the intercept terms, but do not affect reported coefficients. Approximate R-squared statistics are obtained by estimating each equation individually using ordinary least squares regression. The following model is estimated by Seemingly Unrelated Regressions (SUR). = + + + + + + + (3) = 퐸푄 +퐸푄 +퐸푄 +퐸푄 퐸푄+ 퐸푄+ 퐸푄+ + (4) 푖푡 1 푖푡 2 푖푡 3 푖푡 4 푖푡 5 푖푡 6 푖푡 푖푡 ∆퐸푄 =훼 퐶푇퐶 +훽 퐶푇퐶훥퐼푛푣 훽+ 퐼푛푡푎푛퐶푇퐶 훽+ 푇푎푛퐶푇퐶 훽 +퐿푖푞푢푖푑퐶푇퐶 훽 +퐷푒푏푡표푟푠퐶푇퐶 훽 +퐼푛푣푒푛퐶푇퐶 휀 + (5) 푖푡 1 푖푡 2 푖푡 3 푖푡 4 푖푡 5 푖푡 6 푖푡 푖푡 ∆퐶푇퐶 = 훼푆푇퐵 + 훽푆푇퐵 훥퐼푛푣 + 훽푆푇퐵 퐼푛푡푎푛 + 훽푆푇퐵 푇푎푛 + 훽푆푇퐵 퐿푖푞푢푖푑 + 훽푆푇퐵 퐷푒푏푡표푟푠 + 훽푆푇퐵 퐼푛푣푒푛 + 휀 (6) 푖푡 1 푖푡 2 푖푡 3 푖푡 4 푖푡 5 푖푡 6 푖푡 푖푡 ∆푆푇퐵 = 훼푂푆푇 + 훽푂푆푇 훥 퐼푛푣 +훽푂푆푇 퐼푛푡푎푛 +훽푂푆푇 푇푎푛 +훽푂푆푇 퐿푖푞푢푖푑 +훽푂푆푇 퐷푒푏푡표푟푠 +훽푂푆푇 퐼푛푣푒푛 +휀 (7) 푖푡 1 푖푡 2 푖푡 3 푖푡 4 푖푡 5 푖푡 6 푖푡 푖푡 ∆푂푆푇 = 훼 퐸푇퐶 + 훽 퐸푇퐶훥 퐼푛푣 +훽 퐸푇퐶퐼푛푡푎푛 +훽 퐸푇퐶푇푎푛 +훽 퐸푇퐶퐿푖푞푢푖푑 +훽 퐸푇퐶퐷푒푏푡표푟푠 +훽 퐸푇퐶퐼푛푣푒푛 +휀 (8) 푖푡 1 푖푡 2 푖푡 3 푖푡 4 푖푡 5 푖푡 6 푖푡 푖푡 ∆퐸푇퐶 =훼퐿푇퐵 +훽퐿푇퐵 훥 퐼푛푣 +훽퐿푇퐵 퐼푛푡푎푛 +훽퐿푇퐵 푇푎푛 +훽퐿푇퐵 퐿푖푞푢푖푑 +훽퐿푇퐵 퐷푒푏푡표푟푠 훽+퐿푇퐵 퐼푛푣푒푛 휀+ (9) 푖푡 1 푖푡 2 푖푡 3 푖푡 4 푖푡 5 푖푡 6 푖푡 푖푡 ∆퐿푇퐵 = 훼 푂푁퐶 +훽 푂푁퐶훥 퐼푛푣 +훽 푂푁퐶퐼푛푡푎푛 +훽 푂푁퐶푇푎푛 +훽 푂푁퐶퐿푖푞푢푖푑 +훽 푂푁퐶퐷푒푏푡표푟푠 +훽 푂푁퐶퐼푛푣푒푛 +휀 (10) 푖푡 1 푖푡 2 푖푡 3 푖푡 4 푖푡 5 푖푡 6 푖푡 푖푡 ∆푂푁퐶 훼퐿퐷푂 훽퐿퐷푂 훥퐼푛푣 훽퐿퐷푂 퐼푛푡푎푛 훽퐿퐷푂 푇푎푛 훽퐿퐷푂 퐿푖푞푢푖푑 훽퐿퐷푂 퐷푒푏푡표푟푠 훽퐿퐷푂 퐼푛푣푒푛 휀 ∆퐿퐷푂푖푡 훼 훽1 훥퐼푛푣푖푡 훽2 퐼푛푡푎푛푖푡 훽3 푇푎푛푖푡 훽4 퐿푖푞푢푖푑푖푡 훽5 퐷푒푏푡표푟푠푖푡 훽6 퐼푛푣푒푛푖푡 휀푖푡 APPENDIX

An estimate of the actual number of credit days for a firm is given by:

= / . 푡푟푎푑푒 푐푟푒푑푖푡푠 퐶푟푒푑푖푡 푑푎푦푠 푐표푠푡 표푓 푔표표푑푠 푠표푙푑 365 26

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We use trade credits listed on the balance sheet at the end of each fiscal year and cost of goods sold from the income statement to calculate credit days. It is a point estimate of the value of trade credits at the end of the fiscal year. Depending upon the degree of seasonality in purchases, this number may or may not be a good estimate of average trade credits throughout the year. Consider an extreme example of a toy store that pays for purchases of its Christmas stock in November at the due date, of say 90 days. If the fiscal year ends in November, then the amount of trade credits is very large and the estimate of credit days will be correspondingly large. If the fiscal year ends in February, then the estimate of trade credits will be very small. Even in a sample where all firms pay at the due date, the point estimate is affected by random and seasonal variation and on the date chosen for measurement.

To distinguish between cheap and expensive trade credits, we need to determine the standard contract terms in the industry to compare with our estimate of the number of credit days for each firm. We have only a point estimate of the actual credit days at the end of the fiscal year for each company. Two factors affect the calculation of this number. The average number of actual credit days for each industry as an estimate is a good first approximation of the normal contract in that industry. Seasonality and random fluctuations should have little impact on calculating credit days, but the sample distribution contains firms that delay payments on the trade credit. This complication affects the right-hand side of the distribution of credit days and makes the distribution appear somewhat log-normal. Since the values of the mean and median number of credit days are influenced by the number of firms that delay payment, the mean may not be a good estimate of the terms of the contract. If we simply assume a log-normal distribution, we could obtain an estimate of the first moment of the distribution from the average. However, in the discussion that follows, we adopt a simpler method that does not rely on the properties of the distribution.

Assume that most firms choose to pay on time at the end of the contract so they can claim the discount. Then, further assume that the most common number of actual credit days is an estimate of the number of credit days written into the contract for a given industry. The problem of seasonality and randomness in the estimate of actual credit days still exists. It should be removed to isolate the firms with late trade credit payments. Since the right hand of the distribution is influenced by the number of firms with late payment, it is not possible to use the entire distribution to estimate the variance of the number of actual credit days for firms that pay on time. However, it is possible to use the left-hand side of the distribution which contains no late payment firms.

The semi-variance is estimated using the left-hand side of the distribution. It is converted to the variance for the distribution by multiplying by 2. Estimated standard deviation for credit days for each industry is:

= ( (0; ) ) , − 2 푇 2 − 휎 � 푇 ∑푖=1 푀푖푛 푎푐푡푢푎푙 푐푟푒푑푖푡 푑푎푦푠 − 푚푒푎푛 푐표푛푡푟푎푐푡 푑푎푦푠 where T- is the number of firms in the industry that pay on time.

It is now possible to estimate cheap and expensive trade credit for each firm in the sample on an industry- by-industry basis, as listed in the main text as equations (1) and (2):

Cheap Trade Credits: If actual credit days < contract credit days +1.96σ (1)

Expensive Trade Credits: If actual credit days > contract credit days +1.96σ (2)

Figure 1 of the text provides details about the distribution of actual credit days for the entire sample. The median number of credit days is 92 and the average is 106. The mean is larger than the median, reflecting the rightward skewness of distribution due to late payments. Eurofactor (2006) reports an average

J. Bartholdy et al | IJBFR ♦ Vol. 6 ♦ No. 4 ♦ 2012

number of credit days of 83 days for 2005, showing that the number of credit days have declined over time. A priori, we would expect most firms to exploit the discount and pay on time. Thus, an estimate of the due date can be obtained by looking at the mode, or the most common number of credit days (the tallest column in Figure 1). For the entire sample, the mode is between 75 and 85 days. For 2005, Eurofactor (2006) reports an average number of credit days from contracts of 53—so there has been a decrease in actual and contract credit days over time.

Table 1A: Estimation of Credit Days

Industry Sample data Estimate of Estimate of Cut-off number Median number Most common number of credit standard of credit days of days number of days days in a deviation of defining cheap standard credit days and expensive contract credit Food and drinks 60 35-45 40 13.61 66.68 Textiles and clothes 86 66-75 70 28.54 125.94 Wood and paper paste 95 85-95 90 34.54 157.69 Chemical products 116 85-95 90 28.85 146.55

Heavy machinery 108 75-85 80 18.66 116.57 Machinery production and 106 105-115 110 52.20 212.31 equipment

This table shows our estimates for the number of days in a standard trade credit contract for each industry in the third column. The first two columns show the estimates for the median and mode number of credit days for the sample. The fourth column is the standard deviation of estimated credit days in each industry and the fifth column show the number of days for the cutoff between cheap and expensive trade credits for each industry. Firms paying trade credits after the industry cut-off number of days are classified as using Expensive Trade Credits.

The estimate of contract days for each industry is provided in Table 1A and is based on the most common number rounded to a unit of 10s (e.g., 30, 40, etc.). Ng, Smith and Smith (1999) report that the normal contract issued by listed firms (Compustat firms) in the US is 2/10 net 30. That is, a 2% discount is received if paid within 10 days; otherwise payment has to be made within 30 days. The standard deviation of credit days ranges from 13 to 52 days. The cut-off days for cheap credit calculated using equations (1) and (2), as shown in Table 1A ranges from 67 to 212 days. For each firm, if the actual number of credit days is below the cut- off for the industry, then all of its trade credits are classified as Cheap Trade Credits. If the actual number of days is above the estimated industry norm, then all the firm’s trade credits are classified as Expensive Trade Credits.

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BIOGRAPHY

Jan Bartholdy is an Associate Professor of Finance in the Department of Economics and Business at the University of Aarhus in Denmark. His research has appeared in journals such as Journal of Financial

The International Journal of Business and Finance Research ♦ VOLUME 6 ♦ NUMBER 4 ♦ 2012

Research, European Journal of Finance, Accounting and Finance, International Review of Financial Analysis, and Multinational Finance Journal. He can be reached at the Department of Economics and Business, University of Aarhus, Fuglesangs Allé 4, 8210, Aarhus V, Denmark, [email protected].

Cesario Mateus is a Senior Lecturer in Banking and Finance at the University of Greenwich in the UK. His research has appeared journals such as International Review of Financial Analysis, International Research Journal in Finance and Economics, and Emerging Market Letters. He can be reached at the Department of Accounting and Finance, University of Greenwich Business School, Maritime Greenwich Campus, Old Royal Naval College, Park Row, London SE10 9LS, United Kingdom, [email protected].

Dennis Olson is a Professor of Finance at the American University of Sharjah in the United Arab Emirates. His research has appeared in journals such as Journal of Banking and Finance, Financial Review, International Journal of Forecasting, International Journal of Accounting, Economic Inquiry, and European Journal of Economics. He can be reached at the School of Business and Management, American University of Sharjah, PO Box 26666, Sharjah, UAE, [email protected].

The International Journal of Business and Finance Research ♦ VOLUME 6 ♦ NUMBER 4 ♦ 2012

CONVENIENCE YIELDS IN BULK COMMODITIES: THE CASE OF THERMAL COAL Jason West, Griffith University

ABSTRACT

This study advances the research on the convenience yield of bulk commodities with particular emphasis on thermal coal. We extend the option model of Milonas and Thomadakis (1997) to estimate thermal coal convenience yields using forward prices. We examine the business cycle of thermal coal in the presence of both demand and supply shocks and find that the convenience yield for thermal coal exhibits seasonal behavior. Convenience yields are negatively related to the inventory level of thermal coal despite the inventory not being co-located at the point of consumption while convenience yields are positively related to interest rates due to the business cycle. Our estimates of convenience yields for a bulk commodity such as thermal coal is consistent with results for other commodities such as base metals and oil where spot prices are more volatile than forward prices at low inventory levels. The result implies that the costs of storage are generally less than the operating costs associated with changes to production capacity so thermal coal producers prefer to stockpile the commodity rather than adjust production in response to changes in demand.

JEL: C53, G14, Q41

KEY WORDS: Commodities, Convenience Yield, Forward Markets, Thermal Coal, Storage, Options, Contango.

INTRODUCTION

he ability to trade financially settled contracts for seaborne thermal coal has undoubtedly increased the efficiency of the global coal market. Annually more than 560 million tonnes of coal are traded Ton the seaborne market and the growth in the volume of forward contracts for this commodity since 2002 has enabled producers and consumers of thermal coal to smooth out fluctuations in price exposure. This capability is critical for the highly seasonal European power market and the liquid trading of forward electricity contracts. Over 180 million tonnes of thermal coal is imported to Europe annually from the producing regions of South Africa, Colombia, Indonesia and Russia and consumers are able to hedge their exposure through coal swap contracts whose prices are subsequently tracked via a number of key price indexes.

Thermal coal is a strategic resource which is primarily used for power production, although it is also consumed in cement manufacturing and other industries. The security of supply of thermal coal is critical for the efficient operation of Europe’s electricity market. The purpose of this study is to analyze the spot and forward market for thermal coal and provide estimates of convenience yield and volatility through time, while correcting for major sources of seasonality. We find that the observed convenience yield is a surrogate for the volatility level in the thermal coal spot market and changing dynamics in the supply of, and demand for, the commodity.

The concept of convenience yield originates from Kaldor (1939) and was further developed under the theory of storage in Brennan (1958). The theory claims that goods in stock not sold forward have an unobservable value from the flexibility of use, since a market participant owning these goods has the ‘convenience’ to make use of them. For this reason the observable cost of carry of a stored commodity (foregone interest and the cost of physical storage) must be reduced by a so-called availability premium.

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Since buying on the spot market and selling forward is a riskless trade (cash and carry), the (net) cost of carry must equal to the difference between the forward price and the spot price.

When we differentiate the existing body of convenience yield literature by commodity type we discover that bulk commodities such as thermal coal have so far received little attention. This is important to note since comparable studies for other commodities are of limited value for the bulk commodity market. This is because thermal coal is a resource with particular features and even the comparison to other energy commodities such as oil or natural gas, are of limited value. Transportation costs for seaborne thermal coal are generally higher and require significant infrastructure through the value chain. This can lead to persistent demand-supply imbalances when the capacity limit of the transport system is reached resulting in regional price differentials. Besides this, storage of thermal coal is not necessarily co-located with the thermal coal consumer and may therefore incompatible with the notion of convenience. Convenience yield studies in other commodity markets are thus not directly transferable to the coal market due to these unique characteristics. This study uses detailed data on the capacity of the transport network, local production volumes and operating costs for the storage, loading and transport nodes at mine and port facilities. Unlike other empirical studies of supply curves and the production smoothing of inventories we employ actual costs as reported by asset operators which avoids a broad analysis of inventory management where the cost structure is simply assumed.

Extending the option model of Milonas and Thomadakis (1997) to value convenience yields, we find that convenience yields are negatively related to the inventory level of the underlying thermal coal and are positively related to interest rates due to the business cycle. A positive convenience yield can be best represented as a long position in an embedded call option on the commodity. Our convenience yields estimates are consistent with Fama and French (1988) which illustrates that the spot price of thermal coal is more volatile than the forward price at low inventory levels which verifies the Samuelson (1965) hypothesis for bulk commodities.

This paper first discusses commodity markets and thermal coal and then introduces the data. The paper will then outline the model and develop the testing methodology. Finally we present the results and offer some concluding remarks.

LITERATURE REVIEW AND BACKGROUND

Because a commodity can be consumed, its price is a combination of future asset and current consumption values. However, unlike financial assets, storage of energy products is costly and sometimes constrained by infrastructure design. Physical ownership of the commodity carries an associated flow of services and the agent has the option of flexibility with regards to consumption as well as reduced risk of commodity shortages. On the other hand the decision to postpone consumption implies a storage expense. Thermal coal producers operate in an environment where production cannot be altered easily and storage of the commodity is not necessarily co-located at the point of consumption. Furthermore the supply response to changes in demand is notoriously ‘sticky’ implying that production generally continues at a similar rate for some time despite medium-term changes in demand.

In equilibrium, backwardation implies that immediate ownership of the physical commodity entails some benefit or convenience which deferred ownership (via a long forward position) does not. This benefit, expressed as a rate, is termed the ‘convenience yield’. A convenience yield is natural for goods, like art or land, that offer exogenous rental or service flows over time. However, substantial convenience yields are also observed in bulk commodities, such as coal which are consumed at a single point in time. Intuitively, the convenience yield corresponds to the dividend yield for stocks.

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The theory of storage (Brennan, 1958) explains convenience yields in terms of an embedded timing option. In particular, the holder of a storable commodity can decide when to consume it. If it is optimal to store a commodity for future consumption, then it is priced like an asset, but if it is optimal to consume it immediately, then the commodity is priced as a consumption good. Thus, a commodity’s spot price is the maximum of its current consumption and asset values (Routledge, Seppi and Spatt, 2000). Inventory decisions are important for commodities because, by influencing the relative current and future scarcity of the good, they link its current (consumption) and expected future (asset) values. This is unlike equities and bonds where outstanding quantities are generally fixed.

Most studies that estimate convenience yields use a cost-of-carry model where the convenience yield is treated as an exogenous variable. Brennan (1986) showed that convenience yields follow a mean- reverting process while Gibson and Schwartz (1990) used a cost-of-carry model with stochastic mean- reverting convenience yields, assuming the presence of an exogenously defined measure of convenience. Generally, models for convenience yields assume that storage costs are zero (Fama and French, 1988) or are simply assumed (Milonas and Henker, 2001). In particular Fama and French (1988) used the interest- adjusted basis as a proxy which avoids the need to estimate storage costs, to develop the relationship between convenience yield and inventory levels. Taking an alternative approach Milonas and Thomadakis (1997) extended the option approach of Heinkel, Howe and Hughes (1990) using a formulation of the Black–Scholes model to estimate convenience yields. Although they model convenience yields as call options they ignore storage costs which can result in theoretically unjustified negative convenience yields.

We adapt a version of the Milonas and Thomadakis (1997) option pricing model to examine the behavior of convenience yields with supply and demand shocks for thermal coal using free on board (FOB) prices from Richard’s Bay, South Africa. Figure 1 illustrates the weekly average spot price of the price index for thermal coal from Richard’s Bay, known as API4, from 2003 to 2010. A plot of Newcastle FOB prices from Australia is also provided to illustrate the global nature of the seaborne thermal coal market and the relationship between coal prices from different exporting regions.

Thermal coal prices tend to peak in July in preparation for the demand growth for imports to Europe in winter as well as the easing of the monsoon in India where the major ports begin to re-open. The lowest prices for thermal coal generally occur in the northern winter. As with the crude oil and natural gas markets, the behavior of thermal coal is affected by both seasonality and business cycles.

We employ an extension of the Milonas and Thomadakis (1997) option pricing approach because the behavior of thermal coal prices are affected by seasonal business cycles and so modeling price behavior using mean reversion and assuming convenience yields are an exogenous mean-reverting variable (Gibson and Schwartz, 1990) may not necessarily be appropriate. To estimate convenience yields we consider the presence of unexpected demand-supply shocks and the business cycle. We define the business cycle as the sequence of supply and demand equilibrium traced to a point of relative disequilibrium at the height of demand through to the restoration of equilibrium. The thermal coal business cycle is thus from around March to October.

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Figure 1: Mean of API4 (Richards Bay, South Africa) and Newcastle (Australia) Thermal Coal FOB 2003-10 US$/Mt.

90 85 API4(1m) 80 API4(12m)

75 Newc(1m) 70 65 60 US$/Mt FOB US$/Mt 55 50 45 40 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

DATA AND OBSERVATIONS

The main indexes used for the trading, clearing and settlement of thermal coal, jointly calculated and published by Argus and IHS McCloskey, are the API2 and API4 indexes. The API2 index is the international price benchmark for coal imported to north-western Europe while the API4 index is the international price benchmark for coal exported from the Richards Bay Coal Terminal (RBCT) in South Africa. The API4 forward curve is constructed as an average of the Argus FOB Richards Bay assessment and McCloskey’s FOB Richards Bay marker for coal with certain minimum quality specifications. The API4 index is the most appropriate proxy for Atlantic coal prices to the European market because it is more representative of the true cost of coal as a consumption good, it is immune to changes in the forward freight market and it is also immune to supply alternatives from producers that enjoy a freight cost advantage into Europe. The implied inclusion of freight costs in the API2 index are difficult to extract in a meaningful way and therefore a true FOB forward curve is a better representation for this analysis. Table 1 provides a summary of the descriptive statistics of the data used in our analysis.

Table 1: Descriptive Statistics of the Data

1m-API4 (US$/t) 3m-API4 (US$/t) 12m-API4 (US$/t) 3m T-bill (%) Inventory (tonnes) n 1589 1589 1589 1589 1589 Mean 64.98 65.05 66.50 2.63 3.11 Std Dev 29.43 29.11 28.56 1.66 0.68 Skewness 1.94 1.95 1.88 0.07 -0.16 Kurtosis 3.41 3.47 3.45 -1.44 -0.59 Max 189.00 189.80 189.05 5.05 5.00 Min 30.80 30.75 29.65 0.00 1.76 Forward price data for thermal coal is free on board (FOB) Richards Bay (API4) at 1-month, 3-month and 12-month tenors in US$ per metric tonne (US$/t. Data also includes 3-month US T-bills (%) and inventory levels at RBCT in metric tonnes.

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Figure 2: API4 Thermal Coal 12-month forward Price Differential to Spot Price in US$ 2003-10

40% 200 180 30% 12m-1m Contango(+)/Backwardation(-) Spot Price 160 140 20% 120

10% 100 price 80 0% US$/mt

12mto 1m forward 60 40 -10% 20 contango/backwardationas % of cash -20% 0 2003 2004 2005 2006 2007 2008 2009 2010 Source: McCloskey.

Figure 2 shows the contango(+)/backwardation(-) relationship against spot prices from 2003-2011. The 12m rate is used here as a proxy for the longer end of the curve (12-month and 24-month forward prices are highly correlated: > 0.89 over 2003-2011) and the 24-month tenor contracts were relatively illiquid prior to 2005.

If the convenience yield is high enough, the observed forward price will be less than the spot price. This occurs quite frequently in oil and gas markets where the premium for immediacy is very real. If however, this relationship does not hold and the forward price is much higher than spot when taking into account high working capital costs (funding and storage), the convenience yield converges to zero.

In addition to the cost-of-carry theory, Brennan (1958) established an equilibrium model for commodity inventories which assumes that the marginal convenience value of a good is a decreasing function of its aggregate inventory in the economy. Brennan therefore suggests a negative relationship between convenience yield and stock levels, which has been verified empirically for some commodities (Fama and French, 1988; Gibson and Schwartz, 1990; Modjtahedi and Movassagh, 2005) and will be tested for the bulk commodity markets in the following analysis.

THE MODEL

Let ( , ) be the forward price at time t for delivery of the commodity at time T and let ( ) be the spot price. According to the theory of storage under an arbitrage-free framework the return from purchasing the commodity퐹 푡 푇 at t and selling it for delivery at T, ( , ) ( ), will equal the net cost푆 of푡 holding the commodity computed as the interest forgone during storage ( ) ( , ) plus the marginal storage cost ( , ) minus the marginal convenience yield ( , 퐹):푡 푇 − 푆 푡 푆 푡 푅 푡 푇 푊( 푡, 푇) ( ) = ( ) ( , ) + ( , ) ( 퐶, )푡. 푇 (1)

퐹This푡 푇notation− 푆 푡 follows푆 푡 푅Fama푡 푇 and푊 French푡 푇 −(1987).퐶 푡 푇 In a normal market forward prices should exceed spot prices by an amount that is equivalent to interest costs and storage costs and any deviation from this is explained via the so-called convenience yield. This quantity is a marginal spread component which can be modeled as an option on a positive spread between spot and forward prices.

The forward price at date t is determined by current storage levels and the expected demand and production levels at T. When the market experiences higher demand or reduced supply, storage falls to

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zero. If production at T is known with certainty then we expect a direct but negative relationship between the forward price and storage levels which sets an upper bound for the forward price. When supply and demand is in perfect equilibrium we expect the convenience yield to equal zero however when equation (1) holds we obtain ( , ) > 0. A temporary shock in demand or supply conditions during the business cycle will cause a change in storage levels which in turn affects the spot price. This will give rise to a risk premium for possession퐶 푡 of푇 the commodity resulting in a positive convenience yield.

Fama and French (1988) consider the behavior of the convenience yield on an interest-adjusted basis which avoids the need to directly estimate the convenience yield. But this approach fails to provide a complete picture of the true convenience yield. For thermal coal, the storage cost implied in (1) is not difficult to estimate and so observing the true convenience yield is feasible. Using the alternative approach of Milonas and Thomadakis (1997) who treat the convenience yield as an option, we set the spot price as the underlying variable and the price of a 3-month forward contract as the exercise price. Under a cost-of-carry framework with zero storage cost, the convenience yield is the difference between the net cost of carrying a nearby and a distant futures contract observed at time 0,

(0, ) = Max( (0, ) ( , ), 0). (2)

퐶푌The convenience푇 퐹yield푡 from− 퐹 t 푡to푇 T observed at 0 at the commencement of the business cycle ignores the cost of storage. Therefore including the storage cost ( , ) permits equation (2) to be defined as

(0, ) = Max( (0, ) ( , ), 0). 푊 푡 푇 (3) ⋆ where퐶푌 푇 (0, ) = 퐹 (0, 푡) +− 퐹(푡0,푇 ) assuming = 0. ⋆ Since both퐹 the푡 spot퐹 price푡 and푊 the forward푇 price 푡(exercise price) are stochastic we assume they both follow standard diffusion processes which can be expressed as

(0, ) = (0, ) + (0, ) , ( , ) = ( , ) + ( , ) , 푑퐹 푡 휇1푚퐹 푡 푑푡 휎1푚퐹 푡 푑푧1푚 where푑퐹 푡 푇 the 휇subscripts3푚퐹 푡 푇 푑푡1m and휎3푚 퐹3m푡 푇represent푑푧3푚 the 1- and 3-month tenor for each forward contract respectively. We make the important assumption that the diffusion terms are uncorrelated. The associated boundary condition is defined as 푑푧푡 (0, )Max( 1,0), (4)

where퐹 푡 = 퐹푇 (−0, ) ( , ). Applying Ito’s lemma yields the following closed form solution ⋆ (0, 퐹)푇 = 퐹 (0, 푡) ⁄퐹( 푡)푇 ( , ) ( ), (5) ⋆ where퐶푌 푇 퐹 푡 푁 푑1 − 퐹 푡 푇 푁 푑2

( ) = 2 , 푇 c ⁄ ln 퐹 +σ τ 2 ( ) 1 = 휎푐 휏 = 푑 √ 2 , ln 퐹푇 −σcτ⁄2

푑2 푑1 − 휎푐 휏 휎푐√휏 and √

= ( , ) + 2 ( , ) ( , ) ( , ) ( , ) + ( , ), (6) 2 2⋆ ⋆ ⋆ 2 휎푐 휎퐹 0 푡 휎퐹 0 푡 휎퐹 푡 푇 휌퐹 0 푡 퐹 푡 푇 휎퐹 푡 푇 38

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where is the volatility of each forward contract i, is the correlation coefficient of both forward contracts and is the period between the 1-month and the 3-month contracts. This derivation relies on 푖 푖푗 the price휎 of a traded asset as the strike price which resolves휌 the unknown variable problem of the option approach (Lin 휏and Duan, 2007).

RESULTS

We use the 1-month API4 price to represent the spot price of thermal coal since it is the nearest contract for delivery. The 3-month forward API4 price is used to represent the forward price as it is the forward contract with the highest liquidity. For the risk-free rate we use 3-month US Treasury bill yields. We obtained actual storage costs at Richard’s Bay Coal Terminal (RBCT) for the storage cost component of the model. We also make a quality adjustment to the coal at a depletion rate of 120kcal/kg per 3-month period, which acts as a linear price discount for a parcel of coal. No other quality adjustments were made. A non-zero storage cost does not greatly alter the observed behavior in the implied convenience yield curve over time, since storage fees are a small portion of the total cost of thermal coal (US$2-3/t annually).

Convenience yields are calculated on a daily basis throughout the observation month and then averaged over each month. We calculate the monthly convenience yields from January to December and use July, when spot prices peak, as the shock month to estimate convenience yields. We apply a simple regression analysis to examine the relationship between convenience yields and inventory levels, covariance and interest rates and the convenience yield computed using the option formulation of equation (5) and the convenience yield computed using the traditional cost of carry formulation of equation (1). The regression equations are

, = + + , (7) 푂푝푡푖표푛 퐶푌푡 푇 훽0 훽1퐼푡−1 휀푡 , = + + , + + , (8) 퐶표퐶 2 푡 푇 0 1 푡−1 2 푐 푡 3 푡 푡 where퐶푌 훽 is the훽 one퐼 -month훽 휎 lagged훽 푟푓inventory휀 level, is the covariance of the spot and forward contract prices as per equation (6) and is the risk-free rate2 at time t. The theory of storage suggests that holding퐼푡 −inventory1 becomes more costly during periods휎푐 of high interest rates and therefore, convenience yields should be positively related푟푓푡 to the risk-free rate as well as the covariance of the spot and forward prices.

Table 2 presents the convenience yields calculated based on the call option , and cost-of-carry 푂푝푡푖표푛 , models. The results show that the values of the convenience yields estimated from the options 퐶푌푡 푇 model퐶표퐶 are higher than those from the cost-of-carry model, implying the strategic and management 푡 푇 퐶푌flexibility valued using the options approach.

These results give support to the hypothesis of Brennan (1958) and suggest that the convenience yield is highest when inventories are low. That is to say, the benefit of holding inventories is greatest during periods of relative scarcity or heightened demand. In efficient pure contango markets the convenience yield should be close to zero. If inventory levels are small relative to the amount consumed of the commodity, the risk of a supply shock raises the convenience yield. If such risks are high enough, it is expected that the forward market will revert to a backwardated market, often suddenly. Under such conditions, it is also possible that arbitrage conditions may weaken or may even break down. It is incorrect to assume, out of context, that rising inventories means an overhang of supply that translates

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into lower prices until the market clears. Note that forward markets are priced on the principle of equivalence. Table 2: Linear Regression of Convenience Yields for Thermal Coal (API4) 2004-10

Month Model Adj F Jan 0.054 -0.017 0.300ퟐ 46.780 , 휷ퟎ 휷ퟏ 휷ퟐ 휷ퟑ 푹 푂푝푡푖표푛 (0.554) (-0.017) 퐶푌1푚 3푚 0.148*** -0.009*** 2.245*** -0.006*** 0.315 17.409 , 퐶표퐶 (10.866) (-2.116) (6.088) (-3.453) 퐶푌1푚 3푚 Feb 0.042*** -0.010*** 0.099 12.818 , 푂푝푡푖표푛 (5.633) (-3.580) 퐶푌1푚 3푚 0.123*** -0.009*** 1.150*** -0.003*** 0.304 16.579 , 퐶표퐶 (17.281) (-3.274) (4.283) (-2.679) 퐶푌1푚 3푚 Mar 0.012*** -0.013*** 0.079 10.233 , 푂푝푡푖표푛 (3.930) (-3.199) 퐶푌1푚 3푚 0.125*** -0.003*** 2.326*** 0.004*** 0.653 67.985 , 퐶표퐶 (18.829) (-10.194) (9.220) (6.782) 퐶푌1푚 3푚 Apr 0.017*** -0.015*** 0.252 34.060 , 푂푝푡푖표푛 (6.919) (-5.836) 퐶푌1푚 3푚 0.099*** -0.009*** 2.211*** 0.011*** 0.773 91.932 , 퐶표퐶 (12.972) (-10.730) (3.333) (15.299) 퐶푌1푚 3푚 May 0.001 0.000 0.004 1.158 , 푂푝푡푖표푛 (0.888) (-1.319) 퐶푌1푚 3푚 0.167*** -0.026*** 7.107*** 0.014*** 0.589 51.681 , 퐶표퐶 (7.934) (-6.043) (7.861) (11.406) 퐶푌1푚 3푚 Jun 0.027*** -0.046*** 0.112 9.406 , 푂푝푡푖표푛 (2.773) (-3.127) 퐶푌1푚 3푚 0.114*** -0.006*** 4.331*** 0.004*** 0.947 63.253 , 퐶표퐶 (34.902) (-3.820) (29.445) (6.094) 퐶푌1푚 3푚 Jul 0.074*** -0.021*** 0.170 23.360 , 푂푝푡푖표푛 (5.264) (-4.833) 퐶푌1푚 3푚 0.056*** 0.010 1.089*** 0.006*** 0.148 7.330 , 퐶표퐶 (2.021) (1.062) (4.593) (2.872) 퐶푌1푚 3푚 Aug 0.026*** -0.046*** 0.127 17.038 , 푂푝푡푖표푛 (4.867) (-4.128) 퐶푌1푚 3푚 0.073*** -0.015*** 4.852*** 0.029*** 0.431 28.718 , 퐶표퐶 (8.328) (-8.172) (7.004) (8.964) 퐶푌1푚 3푚 Sep 0.011*** -0.061*** 0.091 7.062 , 푂푝푡푖표푛 (2.264) (-3.248) 퐶푌1푚 3푚 -0.111*** 0.017*** -1.623*** 0.007*** 0.853 75.776 , 퐶표퐶 (-5.924) (9.480) (-10.525) (4.250) 퐶푌1푚 3푚 Oct 0.022*** -0.035*** 0.106 11.585 , 푂푝푡푖표푛 (3.797) (-3.404) 퐶푌1푚 3푚 -0.105*** 0.018*** 6.881*** -0.015*** 0.488 29.225 , 퐶표퐶 (-4.883) (6.965) (7.342) (-8.345) 퐶푌1푚 3푚 Nov 0.059*** -0.059*** 0.186 25.149 , 푂푝푡푖표푛 (5.806) (-5.015) 퐶푌1푚 3푚 0.269*** -0.009*** -1.481*** 0.004 0.356 20.540 , 퐶표퐶 (3.958) (-2.548) (-6.897) (0.845) 퐶푌1푚 3푚 Dec 0.015*** -0.014*** 0.156 20.611 , 푂푝푡푖표푛 (5.227) (-4.540) 퐶푌1푚 3푚 -0.048*** 0.015*** 1.553*** 0.017*** 0.737 99.975 , 퐶표퐶 (-4.466) (6.778) (6.442) (11.853) 퐶푌1푚 3푚 Convenience yields are computed using the options estimate and the cost of carry estimate on inventory, volatility and the risk-free rate by month. t-statistics in parentheses, *** denotes significance at the 1 percent level.

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In a perfectly balanced market, a consumer is indifferent between buying a physical commodity now and storing it for later consumption, and buying it for future delivery and letting the producer pay for the storage costs. This situation, also known as full carry, seldom applies in practice. The world's thermal coal consumers, mainly power producers, cannot afford to run out of inventory and they therefore pay for the ‘convenience’ of having excess supplies available. This yield can be viewed as the commodity buyer's insurance payment for supplies. It also represents the producer's cost of hedging by selling forward contracts for the commodity. For bulk commodities such as coal where the cheapest place of storage is generally with the producer, the convenience yield measure could be quite high.

Table 3 illustrates the convenience yields from the shock month of July to the final month of the cycle in November. The negative correlation between the convenience yield and the inventory level suggests that it is closely linked to business cycle, as the convenience yield is unrelated to the thermal coal stocks in Europe.

Table 3: Estimated Convenience Yields of Holding Period 2004-10

Jul-Oct , , 푶풑풕풊풐풏 푪풐푪 2004 0.0779 0.0710ퟏ풎 ퟑ풎 푪풀ퟏ풎 ퟑ풎 푪풀 2005 0.0874 0.0855 2006 0.1198 0.1041 2007 0.0755 0.0783 2008 0.0208 0.0252 2009 0.0073 0.0289 2010 0.0691 0.0595 Total 0.0654** 0.0646** (4.448) (5.8763) Correlation -0.422*** -0.123** p-value <0.001 0.043 휌 Convenience yields are computed from the shock month of July to October (final month of the cycle). t-statistics in parentheses, *** and ** denotes significance at the 1 percent and 5 percent levels respectively.

The theory of storage also predicts that, at a low inventory level, forward prices vary less than spot prices while at a high inventory level, spot prices and forward prices exhibit similar variability. Fama and French (1988) supported Samuelson’s hypothesis by examining the interest-adjusted basis of base metals. The convenience yield declines at higher inventory levels and rises at low inventory levels. To test the Samuelson (1965) hypothesis, we adopt the same approach as Fama and French (1988) and perform a regression of forward prices against spot prices.

Next we conducted a regression of forward prices against spot prices using

, , = + ( ) + , (9)

푡 푇 푡−1 푇−1 0 1 푡 푡−1 푡 categorized푙푛�퐹 ⁄퐹 by high� and훼 low훼 푙푛convenience푆 ⁄푆 yields휀 for both the full sample and also for the shock month (July) data. The data was split by periods of high convenience yield and low convenience yield and the regression analysis then applied to estimate the coefficients. Table 4 shows the results.

We find that high convenience yields have 훼smaller1 average values for the coefficients while low convenience yields have average coefficient values close to one. This implies that at a low inventory level the spot price of thermal coal varies more than the forward price with a high convenience yield derived using the option model approach, while at high inventory levels the spot and forward price of

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thermal coal have similar variability with smaller convenience yields. These results are consistent with the hypothesis of Samuelson (1965) and the results of Fama and French (1988).

Table 4: Regression of Forward Prices against Spot Prices 2004-10

Sample , level , Full High푶풑풕풊풐풏 0.0572푶풑풕풊풐풏*** 0.818*** ퟏ풎 ퟑ풎 ��ퟏ풎 ퟑ풎 휶ퟏ 푪풀 푪풀(5.4199) (2.7031) Low 0.0086*** 0.9149*** (3.4971) (42.557) July as shock High 0.0600*** 0.765*** (4.3043) (34.677) Low 0.0063*** 0.9855*** (5.5945) (89.026)

Regression is estimated using , , = + ( ) + split by high and low convenience yields for the full sample and for the July data. t-statistics in parentheses, *** denotes significance at the 1 percent level. 푡 푇 푡−1 푇−1 0 1 푡 푡−1 푡 푙푛�퐹 ⁄퐹 � 훼 훼 푙푛 푆 ⁄푆 휀 When contracts are far away from maturity they are thinly traded and exhibit low volatility. As the maturity nears, both trading volume and volatility increase. Specifically, spot contracts of thermal coal are usually used for balancing week-to-week needs and consequently exhibit high volatility. This result therefore suggests that the term structure of thermal coal forward volatility is monotonically decreasing.

CONCLUSION

The goal of this paper was to show that for bulk commodities, convenience yields are negatively related to the underlying inventory level and are positively related to interest rates over the business cycle. Using free-on-board thermal coal forward price data over 2003-10 from Richards Bay Coal Terminal (API4) and actual incurred storage costs, our convenience yield estimates were shown to be consistent with the Fama and French (1988) outcomes for more liquidly-traded base metals futures contracts. We have shown that using an extended version of the Milonas and Thomadakis (1997) call option model, the convenience yield for thermal coal exhibits seasonality under the influence of the business cycle. The results show that the negative correlation between the convenience yields for API4 thermal coal and the inventory level at Richard’s Bay becomes more significantly negative when examined during periods of high spot prices, allowing for business cycle effects. This demonstrates that spot prices of bulk commodities are more volatile than forward prices at low inventory levels and verifies that the Samuelson (1965) hypothesis applies for bulk commodities. The results also illustrate that the timing of the business cycle is critical to the calculation of the thermal coal convenience yield.

While interest rates are affected by economic activity they in turn affect convenience yields of thermal coal. We find evidence that supports the Samuelson (1965) hypothesis that spot and forward price variations of thermal coal are similar when a supply shock occurs during higher inventory levels and that spot prices will be more variable than the forward prices at lower inventory levels. Deferred forward contracts are less volatile than near maturity contracts because as a contract draws nearer to maturity, producers and consumers are forced to react more quickly to information shocks and thus the term structure of thermal coal forward volatility is shown to be monotonically decreasing. The implications of the research are that thermal coal producers clearly prefer to stockpile the commodity rather than adjust production in response to changes in demand which implies that the costs of storage are less than the operating costs associated with changes to production capacity.

This approach has a number of limitations which include the usual assumptions of normally-distributed commodity price returns as well as constant implied volatility and a constant risk-free rate as inputs to the extended Black-Scholes option pricing model. This analysis also relies on a liquidly traded forward freight market for bulk commodities to ensure the business cycle is relatively frictionless. Forward

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freight contracts from Richards Bay are generally liquid but there have been periods of low activity which indirectly affects implied forward prices for thermal coal. The level of inventory which underpins the results from the study also relies on frictionless infrastructure to deliver thermal coal to the port from the mines. However there have been delivery bottlenecks over the observation period which may affect the assumed inventory level.

Future research should seek to verify these findings for other bulk markets in the presence of seasonality such as agricultural commodities, alumina and iron ore. Future research may also incorporate stochastic volatility and price mean reversion to more effectively model bulk commodity prices and price volatility to fully test the relationship between convenience yields and inventory over the business cycle.

REFERENCES

Brennan, M.J. (1958) “The supply of storage,” American Economic Review, 48, 50-72.

Fama, E. and French, K. (1987) “Commodity futures prices: some evidence on forecast power, premiums and the theory of storage,” Journal of Business 60: 55-74.

Fama, E. and French, K. (1988) “Business cycles and the behavior of metals prices,” Journal of Finance 43: 1075-1793.

Gibson, R. and Schwartz, E.S. (1990) “Stochastic convenience yield and the pricing of oil contingent claims,” Journal of Finance 45: 959-976.

Heinkel, R., Howe, M. and Hughes, J.S. (1990) “Commodity convenience yields as an option profit,” Journal of Futures Markets 10: 519-533.

Kaldor, N. (1939) “Speculation and economic stability,” Review of Economic Studies 7: 1-27.

Lin, W.T. and Duan, C.W. (2007) “Oil convenience yields estimated under demand/supply shock,” Review of Quantitative Finance and Accounting 28: 203-225.

Milonas, N.T. and Thomadakis, S.B. (1997) “Convenience yields as call options: an empirical analysis,” Journal of Futures Markets 17: 1-15.

Milonas, N.T. and Henker, T., (2001) “Price spread and convenience yield behavior in the international oil market,” Applied Financial Economics, 11, 23-36.

Modjtahedi, M. and Movassagh, N. (2005) “Natural gas futures: bias, predictive performance and the theory of storage,” Energy Economics 27: 617-637.

Routledge, B.R., Seppi, D.J. and Spatt, C.S. (2000) “Equilibrium forward curves for commodities,” Journal of Finance 55(3): 1297-1338.

Samuelson, P.A. (1965) “Proof that properly anticipated prices fluctuate randomly,” Industrial Management Review 6: 41-50.

ACKNOWLEDGEMENTS

The author is grateful for comments made by two anonymous reviewers and to the Institute for Business and Finance Research for inclusion in Global Conference on Business and Finance 2012.

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BIOGRAPHY

Jason West is a Senior Lecturer at the Department of Accounting, Finance and Economics at Griffith University. He also serves as a consultant to the global resources and energy sector. His research appears in journals such as Annals of Actuarial Science, Asia Pacific Financial Markets and the Electricity Journal. He can be reached at Griffith Business School, 170 Kessels Road, Nathan, QLD 4111 Australia, [email protected].

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INTERNATIONAL EVIDENCE ON MARKET LINKAGES AFTER THE 2008 STOCK MARKET CRASH Gulser Meric, Rowan University Christine Lentz, Rider University Wayne Smeltz, Rider University Ilhan Meric, Rider University

ABSTRACT

The 2008 crash was the most important global stock market crash in history since the Great Depression. In this paper, we study the contemporaneous co-movements of and the time-series lead/lag linkages between global stock markets after the 2008 stock market crash by using the time-varying correlation analysis, principal components analysis (PCA), and Granger-causality (G-C) statistical techniques. We find that correlation between global stock markets has increased and the benefit of global portfolio diversification has decreased since the 2008 stock market crash. The PCA technique can group global stock markets in terms of the similarities in their contemporaneous movements. Global investors can maximize the portfolio diversification benefit by investing in stock markets with high factor loadings in different principal components. Our PCA results indicate that all Asian stock markets, except the Japanese stock market, are lumped together in one principal component and the stock markets in the rest of the world are lumped together in another principal component. Our G-C test results show that the U.S. stock market has substantial influence on the European and Australasian stock markets. U.S. stock returns lead the European and Australasian stock returns (i.e., the past returns of the U.S. stock market can predict the future returns of the European and Australasian stock markets).

JEL: G11, G15

KEYWORDS: 2008 Stock Market Crash, Global Stock Market Linkages, Global Portfolio Diversification, Time-Varying Correlation, Principal Components Analysis, Granger Causality

INTRODUCTION

orld economies had a serious financial crisis in 2008. During the October 8, 2007-March 9, 2009 period, major global stock markets experienced the worst crash in history since the Great WD epression. The U.S. stock market fell by about 56 percent during this period. All other major stock markets worldwide experienced similar tailspin.

The stock market crash of 2007-2009, as measured by American markets, began on October 9, 2007 when markets hit their all time highs and concluded on March 9, 2009 when markets bottomed (Wikipedia, 2011). While this financial crisis began in the USA in the fall of 2007, it spread globally within six months. Foreign governments and investors became victims of the globalization of financial markets. Global stock markets in the developed and emerging economies plummeted, reporting worse losses than experienced in decades. The resultant economic uncertainty froze credit, caused large financial institutions to collapse, and major businesses to fail. Foreign direct investment fell substantially as well. Unemployment grew significantly. Between 2007-2008, the Global Stock Market losses totaled $21trillion worldwide (Thompson, 2009).

As the markets began to improve (2009-2010), a recovery pattern emerged: developed economies were hit harder and took longer to rebound, on average, than developing and emerging markets. The S&P BMI

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index for Developed Markets showed a -7.3% annualized return from 2007-2009 as compared to a +4.2% rise in stock market values for Emerging Markets during the same period. Developing and emerging markets had more conservative fiscal policies, and their financial institutions and monetary policies were less integrated with the developed financial markets. “By the time the big crisis in wealthy nations struck, emerging economies were far less vulnerable than many advanced economies. In the panicky months after the fall of Lehman past prudence was not enough to insulate countries from global recession. But once the free fall ended, the emerging world staged a strong recovery, even as advanced economies struggled” (Krugman, 2011).

Table 1 provides evidence that emerging markets rebounded faster and stronger than markets in developed economies. Comparing the October 2007-January 2009 period with the first six months of 2009, the U.S. DJIA moved from -38% to +7.4%, the British FTSE moved from -40% to +8.4 %, and the German DAX from -48% to +20%. The emerging markets improvements were more substantial during the same time period. The Indian market moved from -54% to +63%, the Indonesia market moved from -49% to +49%, and the Chinese markets moved from -67% to +46.3%.

Table 1: Percentage Changes of the Selected Stock Markets during the October 2007-January 2009 and January 2009-June 2009 Periods

% Change % Change Stock Markets Oct 2007 – Jan 2009 Jan 2009 – Jun 2009

DJIA -38 7.4 Malaysia -38 19.9 S&P -40 12.0 U.K. -40 8.4 S. Korea -47 29.5 Germany -48 21.0 Indonesia -49 49.6 France -51 16.0 India -54 63.0 Hong Kong -59 43.0

This table compares the percentage changes in the global stock market indexes during the October 2007-January 2009 crash period with the January 2009-June 2010 recovery period. Source: Zaman, M.R. (2009). ”The Causes and Ramifications of the 2008-2009.Meltdown of the Financial Markets on the Global Economy”. (2009) Eurasian . Journal of Business and Economics, 2(4)63-76. (ONLINE) Available at: http:// www.ejbe.org/EJBE2009Vol02No04p63ZAMAN.pdf (Accessed in May 2011)

World markets followed a similar pattern in that, after the March 2009 bottom, many emerging markets rebounded quite strongly. In particular, the Australasian markets performed well. Specifically, the Indonesian market was the best performer in 2009 with a 87% gain. All sixteen Australasian markets as reported by Reuters (See Table 2) showed gains in 2009 ranging from Indonesia as the best and New Zealand the poorest performing market with a 18.9% gain.

The 2010 results were also positive for all markets but did not match the rate of return of the previous year. There was also not the same overall outperformance of the smaller nations vs. larger nations. However, the poorest performing markets were in larger countries while the best tended to be in smaller countries.

When taking a three-year perspective (2008-2010), the majority of markets had negative returns with the worst being New Zealand while the best market being Thailand with a 8% three year return. It should be noted that this 3 year analysis is somewhat skewed because it begins on January 1, 2008 while the market bottom was reached on March 9, 2009. What it does indicate is that the 2008 crash had such a devastating impact that the impressive returns of 2009 and 2010 could not overcome its impact.

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Table 2: Australasian Stock Markets

% Change % Change % Change Stock Markets in 2009 in 2010 2008-2010

Indonesia 87.0 31.0 7.0 India 81.0 19.0 -6.0 Shanghai 80.0 2.0 -8.0 Taiwan 78.3 18.0 2.0 Singapore 64.5 18.0 0.0 Thailand 63.3 51.0 8.0 Philippines 63.0 30.0 -1.0 Hong Kong 52.0 20.0 -4.0 S. Korea 49.7 25.0 -2.0 Malaysia 44.8 33.0 3.0 Australia 30.9 10.0 -4.0 Japan 19.0 13.0 -6.0 New Zealand 18.9 3.0 -14.0

This table compares the performance of the Australasian stock markets in 2009, in 2010, and during the 2008-2010 period that includes the 2008 crash. * Sources: 1) Vidya Ranganathan, Economic Editor, Asia Desk, Singapore. Thompson Reuters. Personal communication (January 3, 2011). 2) Reuter (2010) “Asia stock markets' 2009 performance; Sri Lanka tops:” retrieved from: http://in.Reuters.com/article/2010/01/01/markets-stocks-idINSGE60000320100101

The objective of this paper is to study the linkages between the world’s major stock markets during the October 8, 2007-July 26, 2010 period with emphasis on the post-2008 crash period. The remainder of the paper is organized as follows: The next section reviews the previous studies investigating the linkages between global stock markets. In the section that follows, we explain our data and methodology. We present our empirical findings in the section titled “Results.” In the last section of the paper, we summarize our conclusions and offer our suggestions for future research.

LITERATURE REVIEW AND BACKGROUND

Studying the co-movements of global stock markets has long been a popular research topic in finance (see Joy et al., 1976, Hilliard, 1979, Maldonado and Saunders, 1981, Meric and Meric, 2011). Low correlation between national stock markets is often presented as evidence in support of the benefit of global portfolio diversification (see Levy and Sarnat, 1970, Solnik, 1974, Lessard, 1976, Watson, 1978, DeFusco et al., 1996).

High correlation and close lead/lag linkages between global stock markets imply less diversification benefit. Principal components analysis has been a popular statistical technique used in empirical studies to study the contemporaneous co-movements of global stock markets (see Philippatos et al., 1983, Meric and Meric, 1989 and 2011, Lee and Kim, 1993). Granger-causality analysis is widely used to study the time-series lead/lag linkages between global stock markets (see Ratner and Leal, 1996, Meric et al., 2002, Meric and Meric, 2011).

Although the globalization of the world’s financial markets has contributed significantly to the welfare of nations, the history of global finance is also replete with many crises (see Christoffersen and Errunza, 2000). Studying eight major stock market crashes during the 1987-2001 period, Wang et al. (2009) find that the world’s stock markets have experienced a major global crash approximately once every three years. Erb et al. (1995), Solnik et al. (1996), Aggarwal and Leal (1997), and Theodossiou et al. (1997) demonstrate that high volatility, which generally occurs during crisis periods, leads to high correlation between national stock markets and reduces the benefits of global portfolio diversification.

Events of global importance can have significant impact on global stock markets. Empirical studies demonstrate that the co-movement patterns of global stock markets change significantly after such events. Roll (1988), Malliaris and Urritia (1992), Arshanapalli and Doukas (1993), Lee and Kim (1993), Lau and

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McInish (1993), and Meric et al. (2001a) study the effects of the 1987 stock market crash on the linkages between national stock markets. They find that the correlation between the word’s stock markets increased and the benefits of global portfolio diversification decreased significantly after the 1987 stock market crash. By studying the effects of the 1997-1998 emerging markets crisis on the co-movements of global stock markets, Meric et al. (2000 and 2001b) and Yang et al. (2003) also find similar results.

The stock market crash of 2008 has received considerable attention in the recent finance literature. Wang et al. (2010 and 2011) study the determinants of stock returns during the 2008 crash. Meric et al. (2010a) compare the performance of exchange-traded sector index funds during the 2008 crash. Meric et al. (2010b) demonstrate that exchange-traded country index funds provided no diversification benefit to global investors during the 2008 stock market crash. The objective of this paper is to study the contemporaneous co-movements of and the time-series lead/lag linkages between national stock market indexes after the 2008 stock market crash.

DATA AND METHODOLOGY

The data of the study are drawn from the Yahoo/Finance database. The study covers all global stock markets in the database. The global stock markets included in the study and the stock market indexes used in the analysis are presented in Table 3.

Table 3: Stock Markets Included in the Study

Stock Market Index United States S&P 500 Latin America Argentina MerVal Brazil Bovespa Mexico IPC Europe Austria ATX France CAC 40 Germany DAX Holland AEX Norway OSE All Share Spain Madrid General Sweden Stockholm General Switzerland Swiss Market United Kingdom FTSE 100 Australasia Australia All Ordinaries China Shanghai Composite Hong Kong Hang Seng India BSE 30 Indonesia Jakarta Composite Israel TA 100 Japan Nikkei 225 South Korea Seoul Composite Malaysia KLSE Composite New Zealand NZSE 50 Singapore Straits Times Taiwan Taiwan Weighted This table presents the list of the stock markets included in the study and the stock market indexes used in the analysis.

Stock markets in different parts of the world have different opening and closing hours. Therefore, weekly returns data are used in the analysis to study the linkages between the world’s stock markets. The weekly stock market indexes are downloaded from the Yahoo/Finance database. The weekly stock returns are computed as the log difference in the indexes (Ln It/It-1).

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Yearly rolling correlations are used to study the contemporaneous time-varying correlation between the U.S., Latin American, European, and Australasian stock markets during the October 8, 2007-July 26, 2010 period. The yearly rolling correlation coefficients are computed by starting with the first week and rolling the sample period ahead one week at a time. Specifically, the latest weekly observation is added while the earliest weekly observation is deleted. A total of 96 rolling correlation coefficients are computed for each pair of stock markets.

The Principal Component Analysis (PCA) technique is applied to the weekly index returns of the twenty- five stock markets to combine the stock markets into distinct principal components in terms of the similarities of their co-movement patterns during the October 8, 2007-July 26, 2010 period. The PCA technique is widely used in the empirical studies to study the co-movement patterns of national equity markets (see: Makridakis and Wheelwright, 1974, Philippatos et al., 1983, Meric and Meric, 1989 and 2011, Lee and Kim, 1993, Lau and McInish, 1993). We examine the global portfolio diversification implications of the co-movements of the markets.

Granger (1969 and 1988) Causality is a useful statistical technique that can be used to study the lead/lag linkages between global stock markets. An independent variable X Granger-causes a change in dependent variable Y, if Y can be better forecasted with past values of both X and Y than just with past values of Y alone. The causality in the Granger sense does not imply a cause and effect relationship, but one of predictability. In empirical studies, the Granger-causality technique is often used to determine if the past index returns of a global stock market can be used to predict the future index returns of another global stock market (see, e.g., Ratner and Leal, 1996, Meric et al., 2002, Meric and Meric, 2011). A detailed discussion of the Granger-causality technique can be found in Enders (1995). We use the Granger- causality technique to study the lead/lag linkages between the U.S., Latin American, European, and Australasian stock markets during the October 8, 2007-July 26, 2010 period.

RESULTS

Time-Varying Correlations

The time varying rolling correlations of the U.S. stock market with the Latin American, European, and Australasian stock markets during the October 8, 2007-July 26, 2010 period are presented in Figure 1. The graphs indicate that there was a high correlation between the U.S. stock market and the Latin American, European, and Australasian stock markets during this period. The correlation coefficient is close to +1.0 with the European markets throughout the period. It shows some volatility with the Latin American and Australasian stock markets.

The correlation coefficient is somewhat low with the Latin American and Australasian stock markets in the fall of 2007. However, when the recessionary conditions became evident in the U.S. economy in the spring of 2008, the correlation coefficient started to climb and it remained at a very high level close to +1.0 throughout the stock market crash until the spring of 2009. When a strong bull market started in the U.S. in the spring of 2009 that continued until the fall of 2009, the correlation between the U.S. stock market and the Latin American and Australasian stock markets began to fall and the correlation coefficient fell below +0.7. The correlation with the European stock markets also fell somewhat during this period. However, it was still well above +0.9.

There was very high, close to +1.0, U.S. correlation with all three other stock markets during the fall of 2009 and the spring of 2010. However, correlation with all three markets fell noticeably in the summer of 2010 reaching +0.9 with the European and Australasian stock markets and slightly below +0.9 with the Latin American stock markets.

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These results indicate that the U.S. stock market has been highly positively correlated with the stock markets in the other parts of the world and the global portfolio diversification opportunities have been limited for U.S. investors during the October 8, 2007-July 26, 2010 period.

Figure 1: The Rolling Correlation of the U.S. Stock Market with the Latin American, European, and Australasian stock Markets.

U.S.-LATIN AMERICA 1

0.9 0.8 0.7 Fall 2007 Summer 2010 0.6 0.5 0.4 0.3 0.2 0.1 0 Correlation Coefficient Time

U.S.-EUROPE 1 0.9 Fall 2007 Summer 2009 Summer 2010 0.8 0.7 0.6 0.5 0.4 0.3 Correlation Coefficient 0.2 0.1 Time 0

U.S.-AUSTRALASIA 1 0.9 0.8 0.7 0.6 Fall 2007 Summer 2010 0.5 0.4 0.3 0.2 0.1

Correlation Coefficient 0 Time In this figure, the first graph shows the time-varying correlation between the U.S. and Latin American stock markets, the second graph shows the time-varying correlation between the U.S. and European stock markets, and the third graph shows the time-varying correlation between the U.S. and Australasian stock markets during the October 8, 2007-July 26, 2010 period.

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Principal Components Analysis

The weekly index returns of the twenty-five stock markets in the sample were used as inputs in the SPSS Principal Component Analysis (PCA) program to study their contemporaneous co-movement patterns during the October 8, 2007-July 26, 2010 period. The PCA technique groups the variables (i.e., the index returns of the stock markets) into principal components in terms of the similarities of their movement patterns. The Varimax rotation was used to maximize the factor loadings of the stock markets in each principal component with similar movement patterns. Using Kaiser's decision rule, statistically significant principal components with eigen values greater than one were retained for analysis (see: Mardia et al., 1979, Marascuilo and Levin, 1983).

Stock markets with high factor loadings in the same principal component are highly correlated. The higher the factor loading of a stock market in a principal component, the higher is its correlation with the other stock markets with high factor loadings in the same principal component. There is less correlation between stock markets with high factor loadings in different principal components. Therefore, to maximize global portfolio diversification benefit, investors should choose stock markets with high factor loadings in different principal components.

There are two statistically significant principal components for the October 8, 2007-July 26, 2010 period. The factor loadings of the two principal components are presented in Table 4. The cumulative variance explained by the two principal components is 74.344% (i.e., the two principal components explain 74.344% of the total variance in the original data matrix). The first principal component explains 66.596% of the total variance. The second principal component explains 5.797% of the total variance.

Table 4: Principal Components Analysis

Principal Components Stock Markets Prin Comp #1 Prin Comp #2 U.K. 0.937 France 0.931 Germany 0.906 U.S. 0.897 Holland 0.893 Switzerland 0.887 Sweden 0.875 Spain 0.873 Austria 0.843 Brazil 0.799 Mexico 0.796 Norway 0.782 Australia 0.730 Argentina 0.716 Japan 0.708 New Zealand 0.650 Malaysia 0.772 Indonesia 0.680 Hong Kong 0.664 Taiwan 0.663 Shanghai 0.650 Singapore 0.645 Israel 0.623 South Korea 0.619 India 0.602 Eigen Value 16.649 1.449 Variance Explained 66.596% 5.797% CumVar Explained 66.596% 74.344% This table presents the factor loadings of the stock markets in the two statistically significant principal components during the October 8, 2007- July 26, 2010 period. Kaiser’s significance rule was used in picking the two principal components for further analysis. According to this rule a principal component is statistically significant if its eigen value is greater than one.

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The first principal component is dominated by the U.S., European, and Latin American stock markets. The Australian, Japanese, and New Zealand stock markets also have high factor loadings in the first principal component. All the stock markets with high factor loadings in this principal component are highly correlated and they were not good global portfolio diversification prospects for investors during the October 8, 2007-July 26, 2010 period.

Most Asian stock markets have high factor loadings in the second principal component. Investing in a portfolio consisting of these stock markets would not provide significant diversification benefit to global investors during the October 8, 2007-July 26, 2010 period. However, these stock markets were good global portfolio diversification prospects for the investors of countries with high factor loadings in the first principal component, including the U.S. investors, during this period. Similarly, the stock markets with high factor loadings in the first principal component could provide significant global portfolio diversification benefits to the investors of the stock markets with high factor loadings in the second principal component.

Linkages between the Markets

In this section of the paper, we study the lead/lag relationships between the U.S., Latin American, European, and Australasian stock markets with the Granger (1969, 1988) causality technique. The Sims (1980) test indicates that optimal lag-length is three trading weeks in the vectorautoregression model used in the analysis.

The time-series ordinary least squares regression equations used in the Granger-causality tests are as follows:

USt = a0 + a1USt-1 + a2USt-2 + a3USt-3 + a4LAt-1 + a5LAt-2 + a6LAt-3 + a7EUt-1 + a8EUt-2 + a9EUt-3 + a10AUt-1 + a11AUt-2 + a12AUt-3 + et (1)

LAt = b0 + b1LAt-1 + b2LAt-2 + b3LAt-3 + b4USt-1 + b5USt-2 + b6USt-3 + b7EUt-1 + b8EUt-2 + b9EUt-3 + b10AUt-1 + b11AUt-2 + b12AUt-3 + ft (2)

EUt = c0 + c1EUt-1 + c2EUt-2 + c3EUt-3 + c4USt-1 + c5USt-2 + c6USt-3 + c7LAt-1 + c8LAt-2 + c9LAt-3 + c10AUt-1 + c11AUt-2 + c12AUt-3 + vt (3)

AUt = d0 + d1AUt-1 + d2AUt-2 + d3AUt-3 + d4USt-1 + d5USt-2 + d6USt-3 + d7LAt-1 + d8LAt-2 + d9LAt-3 + d10EUt-1 + d11EUt-2 + d12EUt-3 + zt (4)

where US stands for U.S. stock market index returns at year t, LA stands for Latin American stock market index returns at year t, EU stands for European stock market index returns at year t, and AU stands for Australasian stock market index returns at year t; a0, b0, c0, and d0 are the constants in the regressions; a1 through a12 in the first equation, b1 through b12 in the second equation, c1 through c12 in the third equation, and d1 through d12 in the fourth equation are the regression coefficients of the explanatory variables; et, ft, vt, and zt are the white-noise error terms in the regressions.

The Granger-causality test results for the joint hypotheses of zero coefficients on all three lags for each stock market are presented in Table 5. In the test with the U.S. stock market as the dependent variable in Panel A of the table, the F statistics for the European and Australasian stock markets are not statistically significant. It indicates that past returns of the European and Australasian stock markets cannot be used to predict the future returns of the U.S. stock market. However, the F statistic for the Latin American stock markets is significant at the 10% level. It indicates that the past stock returns of the Latin American stock markets have somewhat strong power in predicting the future returns of the U.S. stock market.

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The Latin American stock markets are the dependent variable in panel B of the table. The F statistics for the U.S., European, and Australasian stock markets are all significant at the 10% level in this test. It indicates that the past returns of the U.S., European, and Australasian stock markets all have some predictive power of the future returns of the Latin American stock markets. The p value statistics imply that the Australasian stock returns have more predictive power compared with the European and U.S. stock returns.

Table 5: Granger Causality Tests

Stock Market F Statistic P Value Panel A: Dependent Variable: United States1 Latin America 2.5733* 0.0568 Europe 0.9711 0.4085 Australasia 0.7050 0.5507 Panel B: Dependent Variable: Latin America2 United States 2.2918* 0.0812 Europe 2.4823* 0.0638 Australasia 2.5689* 0.0571 Panel C: Dependent Variable: Europe3 United States 7.1241*** 0.0002 Latin America 3.3328** 0.0216 Australasia 1.1540 0.3299 Panel D: Dependent Variable: Australasia4 United States 6.0014*** 0.0007 Latin America 1.4554 0.2300 Europe 2.0611 0.1085 This table presents the Granger-causality regression results. ***, **, and * indicate the significance of the F statistic for the explanatory variables at the 1, 5, and 10 percent levels, respectively. 1 Centered R2 = 0.969 2 Centered R2 = 0.973 3 Centered R2 = 0.974 4 Centered R2 = 0.985

The European stock markets are the dependent variable in Panel C. The F statistic for the Australasian stock markets is not statistically significant in this test. However, the F statistics for the U.S. stock market is significant at the 1% level and the F statistic for the Latin American stock markets is significant at the 5% level. These results indicate that the U.S. and Latin American stock returns lead the European stock markets (i.e., the past returns of the U.S. and Latin American stock markets can predict the future returns of the European stock markets).

The Australasian stock markets are the dependent variable in Panel D. The Latin American and European stock markets are not statistically significant at the conventional levels in this test. However, the F-value test statistic for the U.S. stock market indicates that U.S. stock market has a strong lead over the Australasian stock market (the past returns of the U.S. stock market can predict the future returns of the Australasian stock markets with a high degree of accuracy).

CONCLUDING COMMENTS

The 2008 crash was the most important global stock market crash in world history since the Great Depression. Previous empirical studies demonstrate that events of global importance can change the correlation patterns of global stock markets. In this paper, we study the linkages between global stock markets after the 2008 stock market crash with the time-varying correlation analysis, principal components analysis, and Granger-causality statistical techniques. Global portfolio diversification is recommended because of low correlation between global stock markets. Our time-varying correlation analysis results indicate that the correlation between global stock markets has increased and the benefit of global portfolio diversification has decreased considerably after the 2008 stock market crash.

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The principal components analysis (PCA) technique can group global stock markets in terms of the similarities in their contemporaneous co-movements. Global investors can maximize the portfolio diversification benefit by investing in stock markets with high factor loadings in different principal components. Our PCA results indicate that all Asian stock markets, except the Japanese stock market, were lumped together in one principal component and the stock markets in the rest of the world were lumped together in another principal component during the October 8, 2007-July 26, 2010 period. This implies that there were relatively limited global portfolio diversification opportunities available for investors during this period. Investors could benefit from global portfolio diversification by investing in a stock market with high factor loading in the first principal component and in another stock market with a high factor loading in the second principal component.

Close lead/lag linkages between global stock markets imply limited portfolio diversification benefits to global investors. Our Granger-causality test results indicate that the U.S. stock market has substantial influence on the European and Australasian stock markets after the 2008 stock market crash. U.S. stock returns lead European and Australasian stock returns with a high level of statistical significance (i.e., the past returns of the U.S. stock market can predict the future returns of the European and Australasian stock markets with a high degree of accuracy) after the crash. There is also close lead/lag linkages between the U.S stock market and the Latin American stock markets. These results indicate that there are limited global portfolio diversification opportunities for U.S. Global investors after the 2008 global stock market crash.

This paper focuses on the contemporaneous co-movements and time-series lead/lag linkages between the world’s stock markets only during the October 8, 2007-July 26, 2010 period. Future research investigating the changes in the long-term correlation and lead/lag linkage patterns of global stock markets can provide valuable information. Such a study can help determine if world financial crises are causing permanent structural breaks in the global stock market relationships.

In this paper, we use the time-varying correlation analysis, principal components analysis, and Granger- causality techniques to study the co-movements of the world’s stock markets during and after the 2008 crash. An interesting field of study in finance is the long-term integration of the world’s stock markets. The co-integration analysis technique is generally used for this purpose to study the co-integration of pairs of stock markets. A future study focusing on the long-term co-integration of the U.S., Latin America, European, and Australasian stock markets can provide valuable insights to investors in terms of long-term global portfolio diversification prospects.

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BIOGRAPHY

Dr. Gulser Meric is a Professor of Finance at Rowan University. She can be contacted at: Department of Accounting and Finance, Rohrer College of Business, Rowan University, Glassboro, NJ 08028. Email: [email protected].

Dr. Christine Lentz is an Associate Professor of Management and Human Resources at Rider University. She can be contacted at: Department of Management and Human Resources, College of Business Administration, Rider University, Lawrenceville, NJ 08648. Email: [email protected].

Dr. Wayne Smeltz is an Associate Professor of Business Policy at Rider University. He can be contacted at: Department of Business Policy, College of Business Administration, Rider University, Lawrenceville, NJ 08648. Email: [email protected].

Dr. Ilhan Meric is a Professor of Finance at Rider University. He can be contacted at: Department of Finance and Economics, College of Business Administration, Rider University, Lawrenceville, NJ 08648. Email: [email protected].

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THE PRICE RESPONSE TO NIKKEI 225 STOCKS INDEX ADJUSTMENTS Chia-Jung Tu, Kainan University

ABSTRACT

Using Nikkei 225 Index adjustment data, this study examines price response to changes in index composition. This study demonstrates that prices of stocks added to and deleted from the Nikkei 225 Index respectively fluctuate accordingly on the announcement day. These price trends then reverse during the post-announcement period. The results are consistent with the price pressure hypothesis. By classifying the composite stocks into two categories, this study finds that small-scale stocks exhibit larger price responses than large-scale stocks. In addition, the results show that newly added stocks with upward revised earnings forecasts earn more abnormal returns during the post-announcement period. The results shed more light on the information content associated index adjustments in the Japanese stock market.

JEL: G15；G32

KEYWORDS: Nikkei 225 Index, price responses, earnings forecast revision

INTRODUCTION

umerous studies have examined the price responses of stocks to index adjustments. Harris and Gurel (1986), Shleifer (1986), Wurgler and Zhuravskaya (2002) and Chen et al. (2004, 2006) N examined changes in composition of the S&P 500 Index. Some studies have focused on non-U.S. stock indices. For instance, Chakrabarti et al. (2005) applied a widely used set of country equity indices, the MSCI country indices, for 29 countries to the returns of stocks added to or removed from indices around the event dates. They found that stock returns and volumes exhibit an “Index effects” in international markets.

The Tokyo Stock Exchange is the largest stock exchange in South East Asia and the third largest in the world by market capitalization. However, little attention has focused on changes in the composition of the most broadly quoted Japanese stock index, the Nikkei 225 Index. Hanaeda and Serita (2003) examine large composite change in the Nikkei 225 Index that occurred on April, 2000. But, they only consider a single event change. Okada et al. (2006) used a large Nikkei 225 Index sample from 1991 through 2002 to investigate the stock price and volume behavior of firms around the time of their addition to the index. However, they did not consider the stock price and volume behavior of deleted firms. This study uses the composition changes in the Nikkei 225 Index to study the pricing effects.

Liu (2000) examined the effects of changes in the Nikkei 500 Index on stock prices and trading volume. He found significant price increases (decrease) for added (deleted) stocks with no post-event reversal (The event window is -15 to +15 days). Furthermore, Okada et al. (2006) used the Nikkei 225 Index to investigative the stock price and volume behavior of firms around their addition to the index. They found the stock prices of firms added to the Nikkei 225 increased on the announcement date, continue to increase until the day before the effective change date, and then decrease on and immediately after the change date. This occurred on average, approximately five business days between the announcement date and the actual change date. The Nikkei 225 and 500 Indices are price-weighted averages of 225 and 500 actively traded stocks on the first section of the Tokyo Stock Exchange. The reason that different studies have obtained different empirical results is unclear, but possibly may be due to the fact that the Nikkei 500 includes more small-cap stocks than the Nikkei 225. This study attempts to separate the added and deleted stocks into two types depending on market value. Next we examine which firm types exhibit larger price responses. Besides categorizing composite stocks into different market value segments, this study also classifies firms using analyst earnings forecasts to explore the price reactions of

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upwards or downwards earnings forecast revisions of firms added to the Nikkei 225. For the firms deleted from the Nikkei 225 Index, the earnings forecast data is limited. Because of this data limitation, this study examines only earnings data of added firms.

The analytical results show that the price responses of stocks experiencing adjustments in the Nikkei 225 are consistent with the price pressure hypothesis. When classifying the characteristics of composite stocks into two categories, this study finds that small-scale stocks exhibit larger price responses than large-scale stocks. Moreover, added stocks with upwards earnings forecast revisions have abnormal returns than the added stocks with downwards earnings forecast revisions during the post-announcement period. This finding suggests that investors can profit by buying added stocks with upwards revision earnings forecasts.

The remainder of the article is organized as follows. Section 2 discusses the literature review. Section 3 describes the sample and methodology that I use. Section 4 presents and discusses the empirical results. Section 5 concludes the article.

LITERATURE REVIEW

The literature on the effects of price changes due to the addition and deletion of stocks to major stock indices is voluminous. Harris and Gurel (1986), Shleifer (1986), Dhillon and Johnson (1991), Wurgler and Zhuravskaya (2002) found strong price effects for S&P 500 inclusions. Kaul et al. (2000) and Okada et al. (2006) found similar effects on the Toronto Stock Exchange TSE 300 and Nikkei 225 indices.

Harris and Gurel (1986) found strong effects for S&P 500 inclusions, but unlike the permanent volume effect, the price effect is reversed over time. They therefore summarized that these effects are due to price pressure. Shu et al. (2004) found additions (deletions) to the MSCI free indices have a positive (negative) abnormal return in the run-up window from the announcement day up to one day before the change was implemented. This was followed by a significant reversal on the change day. Shankar and Miller (2006) found that firms added to the S&P 600 index experience a significant price increase at announcement. However, the price and volume effects are temporary and are fully reversed within 60 days. Okada et al. (2006) found the stock prices of firms to be added, rise on the announcement date, continue to rise until the day before the effective change date, and subsequently decline beginning on the change date. Hence their results also support the temporary price-pressure hypothesis.

On the other hand, Shleifer (1986) found permanent price changes and attributes them to the downward sloping demand curve for stocks and the fact that stocks are imperfect substitutes for one another. Wurgler and Zhuravskaya (2002) witnessed that stocks with no close substitutes experience a higher increase in returns on inclusion in the S&P 500 index. This finding corroborates evidence for the downward sloping demand curve view. Kaul et al. (2001) and Liu (2000) also reported results consistent with the downward sloping demand curve hypothesis based on the Toronto Stock Exchange 300 and Nikkei 500.

Jain (1987) and Dhillon and Johnson (1991) argued there may be an information effect in the inclusion or exclusion of stocks to a major index. Denis et al. (2003) also suggests that S&P Index inclusion is not an information-free event. The liquidity hypothesis suggests the price increase at index inclusion results from increased liquidity. Hedge and McDermott (2003) found there is a negative relationship between cumulative abnormal returns around the announcement and the percent change in the spread. The results show that when trading costs decrease because of liquidity stocks have higher returns.

Chen et al. (2004, 2006) studied the price effects of changes to the S&P 500 Index and witnessed an asymmetric price response. Consistent with prior work, they found a permanent price increase for firms added to the S&P 500 Index. However, they found that firms deleted from the index do not experience a permanent negative price effect. They argue that a possible reason for asymmetric price response effects arises from changes in investor awareness.

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DATA AND METHODOLOGY DESCRIPTION

This study uses stocks either added to or removed from the Nikkei 225 Index to study the effects of index composition changes during the periods from September 1991 to March 2008. Table 1 lists these changes by year. Excluding insufficient price data during the event periods, the final samples comprised 88 firms added to and 51 firms deleted from the Nikkei 225 Index. Besides exploring price changes for the whole sample, this study also used the characteristics of composite stocks as a basis for dividing the sample firms into two categories. Category 1 separates composite stocks into large scale and small scale firms depending on their market capitalization. Category 2 classifies newly added firms based on analyst earnings forecasts into upwards and downwards earnings adjustment groups. To examine which firm types exhibit larger price changes effect. Earnings per share forecasts are obtained from the I/B/E/S database. The price and market value of Japanese stocks are obtained from DataStream. Information on announcement dates for the Nikkei 225 Index adjustment is obtained from the Nikkei Interactive website. This study uses the TOPIX index as the Japanese market index. Liu (2000) used TOPIX index as market index to investigate the price and trading volume effects of changes in the Nikkei 500. Okada et al. (2006) used the TOPIX index as a market index to study the stock price and volume behavior of firms around the time of their addition to the Nikkei 225 Index. This study follows the practices of using TOPIX as Japan market index.

An event study approach is applied in this study. Using the market model, the return of stock i on day t, denoted as Rit , is calculated as:

Rit = αi + βi Rm + ε it (1)

where Rmt is the return of the Japan market on day t. The parameters of the market model are estimated using an Ordinary Least Squares (OLS) regression.

Table 1：Changes in the Nikkei 225 Index, September 1991 to March 2008.

Nikkei 225 Index Year ＃of Changes 1991 6 1992 4 1993 2 1994 0 1995 1 1996 2 1997 1 1998 2 1999 2 2000 38 2001 13 2002 12 2003 5 2004 4 2005 10 2006 4 2007 3 2008 3 Total 112 This table shows the changes of Nikkei 225 Index adjustment during the periods from September 1991 to March 2008.

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These parameters are then used to calculate abnormal returns associated with the event examined. I choose an estimation period of -120 to -11 days and an event window of -10 to 30 days. The abnormal return of stock i on day t, is denoted as ARit , and is calculated as: AR = R − Rˆ = R − (αˆ + βˆ R ) (2) it it it it i i mt ˆ where Rit the expected is return of stock i on day t. The mean abnormal return across the sample, denoted as MARt is defined as:

N ARit MARt = ∑ (3) i=1 N

This study then computes the cumulative abnormal return (CARi) for stock i for various event windows from t = j to t = k as

k CARi = ∑ ARit (4) t= j

The mean cumulative abnormal return (CAR) of N stocks is

N ∑CARi CAR = i=1 (5) N

The corresponding t-statistics that measure whether the CAR is significantly different from zero is

CAR t(CAR) = , (6) Var(CARi ) N

where Var(CARi) is the variance of CARi among N stocks.

This study also uses a two-sample t-statistics to test whether different CARs in two subsamples are equal to each other. The t-statistics of two-sample mean cumulative abnormal return is as follows:

CAR − CAR t = S1 S 2 , (7) S 2 S 2 S1 + S 2 NS1 NS 2

where S1 is the subsample 1, S2 is the subsample 2, CARS1(CARS 2 ) is the CAR of subsample 1 2 2 (subsample 2), S S1 (S S 2 ) is the variance of CARi in subsample 1 (subsample 2), and N S1(N S 2 ) is the number of stocks in subsample 1 (subsample 2).

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EMPIRICAL RESULTS

Price Responses Displayed by Stocks Added to the Nikkei 225

Mean abnormal returns (MAR) and mean cumulative abnormal returns (CAR) are listed in Table 2 (Panel A) and Table 3 (Panel A). Figure 1 shows the trend of mean cumulative abnormal returns for 88 added stocks in the event-period. This study first focuses on the price responses of added stocks as reported in Table 3 (Panel A) and makes three observations with respect to the abnormal returns. The first observation relates to the pre-announcement period abnormal returns. During the period from day -10 to day -1, the mean abnormal returns are not significantly different from 0. The second observation relates to the announcement day abnormal return. The mean abnormal return on the announcement day is 1.81%, which is significant at 1% level. This result is similar to those found in earlier studies.

The third observation relates to the post-announcement period abnormal returns. The results show the abnormal returns are still positive and significant from day 1 to day 4. However, after day 4, the added firms experience significant negative abnormal returns of -2.01%, with a t-value of -4.41 on day 5. The negative abnormal returns continue for several days. Also, Table 3 (Panel A) displays the mean cumulative abnormal returns for 88 added stocks fully reverses from day 2 to day 20. The manner is consistent with Harris and Gurel (1986) who examine the no-information assertion. The results suggest that the price response of added firms in the Nikkei 225 Index is consistent with the price pressure hypothesis. These results for the Nikkei 225 Index are similar to those of Harris and Gurel (1986), Shu et al. (2004) and Okada et al. (2006).

Figure 1： Mean Cumulative Abnormal Returns for Stocks Added to the Nikkei 225, 1991-2008

0.1 CAR chart for a (-10, 30) window 0.08

0.06

0.04

CAR 0.02

0 -10 -5 0 5 10 15 20 25 30 -0.02 event window

This figure shows the trend of mean cumulative abnormal returns for 88 stocks added to the Nikkei 225 in the event-period from 1991 to 2008.

Price Responses of Large-Scale and Small-Scale Stocks Added to the Nikkei 225

Next, this study divides the composite stocks into large-scale and small-scale depending on their market value, and examines whether the stock price responses of the two subsamples are the same. Table 4 shows the mean cumulative abnormal returns for 44 large-scale added stocks and 44 small-scale added stocks in various event periods. Figure 2 shows the trend of mean cumulative abnormal returns of the two subsamples of added stocks in the event-period. The dashed line represents the small-scale stocks and the solid line depicts the large-scale stocks. Large capitalization added stocks achieve a significant abnormal return of 2.73 % on the announcement day and significant CARs for the periods from day 1 to day 5, day -10 to day 15 and day -10 to day 30.

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Table 2：The Price Responses of Stocks Added to or Deleted from the Nikkei 225 Index, 1991-2008.

Event Panel A: Additions Panel B: Deletions

Day #Obs MAR t(MAR) #Obs MAR t(MAR) -10 88 0.15% 0.498 51 -0.28% -0.683 -9 88 0.12% 0.501 51 2.69%*** 5.269 -8 88 0.39% 1.622 51 -0.78%*** -2.544 -7 88 0.32% 1.344 51 0.64%* 1.836 -6 88 0.02% 0.056 51 0.14% 0.447 -5 88 -0.32% -1.677 51 -0.53% -1.472 -4 88 0.29% 1.171 51 1.44%*** 4.311 -3 88 -0.31% -1.112 51 1.13%*** 2.540 -2 88 0.31% 1.017 51 -0.44% -0.796 -1 88 -0.15% -0.641 51 -0.78% -1.590 0 88 1.81%*** 4.460 51 -13.13%*** -7.108 1 88 4.27%*** 9.627 51 -14.13%** -1.965 2 88 0.85%** 2.213 51 -4.10%*** -5.514 3 88 0.32% 0.738 51 -3.09%*** -3.599 4 88 1.99%*** 3.442 51 4.20%*** 4.761 5 88 -2.01%*** -4.411 51 0.86% 1.664 6 88 -0.79%*** -2.848 51 -0.88%** -2.065 7 88 -0.31% -1.232 51 -1.88%*** -3.732 8 88 0.31% 0.910 51 -0.39% -0.902 9 88 -0.73%* -1.893 51 2.33%*** 4.150 10 88 0.12% 0.288 51 1.22%*** 3.276 11 88 -0.25% -0.836 51 0.36% 0.459 12 88 -0.19% -0.770 51 -0.90%** -2.244 13 88 0.16% 0.683 51 -2.93%*** -5.267 14 88 -0.32% -1.289 51 1.25%*** 2.458 15 88 -0.41% -1.592 51 0.81% 1.580 16 88 0.12% 0.444 51 1.64%*** 3.336 17 88 -0.03% -0.117 51 1.20%*** 3.779 18 88 -0.14% -0.501 51 -0.44% -1.015 19 88 -0.49%* -1.936 51 -0.52% -1.516 20 88 -0.38% -1.542 51 -2.40%*** -6.268 21 88 0.07% 0.237 51 0.01% 0.027 22 88 0.66%** 2.062 51 -0.53%** -2.250 23 88 0.31% 1.412 51 0.96%*** 2.622 24 88 -0.23% -0.827 51 -0.20% -0.603 25 88 0.19% 0.683 51 0.51% 1.298 26 88 -0.49%* -1.695 51 -0.72%*** -2.659 27 88 0.10% 0.449 51 -0.14% -0.601 28 88 -0.42%* -1.734 51 0.50%*** 2.387 29 88 0.08% 0.323 51 1.67%*** 3.723 30 88 0.54%** 2.190 51 0.46% 1.530 This table shows the mean abnormal returns (MAR) around the announcement date for 88 stocks added to and 51 stocks deleted from the Nikkei 225 Index in 1991- 2008. Day 0 denotes the announcement day. ***, **, and* indicate significant at 1, 5 and 10 percent levels respectively.

Small capitalization added stocks also show a significant positive abnormal return on the announcement

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day, but of a lower magnitude (0.88%). CARs of small capitalization stocks are positive and significant in the other five event periods. The two-sample t-statistics indicate a significantly different on the announcement day. In other event periods, the CARs for small-scale added stocks are bigger than those of large-scale stocks, although they are not statistically significant. The results show that small-scale added stocks seem to have larger price responses. Figure 2 illustrates that the trend of mean cumulative abnormal returns for the large-scale added stocks closely resemble those of the whole added sample.

Tables 3： Mean Cumulative Abnormal Returns from Day 2 to Day T for Stocks Added to or Deleted from the Nikkei 225 Index, 1991- 2008.

Day 2 to Panel A: Additions Panel B: Deletions

Day T CAR t-value p-value CAR t-value p-value

2 0.85%** 2.213 0.030 -4.10%*** -5.514 0.000 3 1.17%* 1.881 0.063 -7.18%*** -5.794 0.000 4 3.16%*** 3.494 0.001 -2.98%*** -3.668 0.001 5 1.16% 1.464 0.147 -2.12%* -1.986 0.053 6 0.37% 0.510 0.611 -3.01%** -2.509 0.015 7 0.06% 0.073 0.942 -4.89%*** -3.294 0.002 8 0.37% 0.432 0.667 -5.28%*** -3.072 0.003 9 -0.36% -0.379 0.706 -2.95%** -2.057 0.045 10 -0.24% -0.250 0.803 -1.73% -1.335 0.188 11 -0.49% -0.510 0.612 -1.38% -0.961 0.341 12 -0.69% -0.686 0.495 -2.28% -1.426 0.160 13 -0.52% -0.502 0.617 -5.20%*** -3.046 0.004 14 -0.84% -0.819 0.415 -3.95%** -2.167 0.035 15 -1.25% -1.207 0.231 -3.15% -1.662 0.103 16 -1.13% -1.043 0.300 -1.51% -0.875 0.386 17 -1.16% -1.029 0.306 -0.31% -0.180 0.858 18 -1.30% -1.135 0.259 -0.76% -0.420 0.676 19 -1.79% -1.501 0.137 -1.28% -0.690 0.494 20 -2.18%* -1.742 0.085 -3.68%* -1.958 0.056 21 -2.11%* -1.671 0.098 -3.67%* -1.929 0.059 22 -1.45% -1.113 0.269 -4.21%** -2.174 0.034 23 -1.14% -0.855 0.395 -3.25% -1.568 0.123 24 -1.37% -1.009 0.316 -3.45% -1.577 0.121 25 -1.18% -0.828 0.410 -2.94% -1.353 0.182 26 -1.67% -1.185 0.239 -3.66% -1.618 0.112 27 -1.57% -1.110 0.270 -3.79%* -1.738 0.088 28 -1.99% -1.361 0.177 -3.29% -1.506 0.138 29 -1.91% -1.307 0.195 -1.62% -0.746 0.459 30 -1.37% -0.935 0.353 -1.16% -0.516 0.608 This table shows the mean cumulative abnormal returns from day 2 to day T for stocks added to or deleted from the Nikkei 225 Index in 1991- 2008. Mean cumulative abnormal returns(CAR) from day 2 to day T for 88 stocks added to and 51 stocks deleted from the Nikkei 225 Index in 1991- 2008, ***, **, and* indicate significant at 1,5 and 10 percent levels respectively.

Price Responses of Upwards and Downwards Revisions of Earnings Forecasts for Stocks Added to the Nikkei 225

Sixty-one stocks were added to the index during the study period for which analysts had made earnings

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forecasts. Stocks for which insufficient price data exists during the event periods are excluded. The final sample comprises 59 stocks from the Nikkei 225 Index for which analysts have made earnings forecasts. This study calculates changes in earnings forecasts for the stocks by subtracting the pre-announcement earnings forecast from the post-announcement earnings forecast. To compute the pre-announcement and post- announcement median EPS forecasts of an Index inclusion for a given company, the event window is four months prior to the announcement month of an Index inclusion and four months following to the announcement month of an Index inclusion. For each individual analyst, this study uses the pre-announcement EPS forecast, which is closest to the announcement month of an Index inclusion. From these individual analysts’ forecasts, the median pre-announcement EPS forecast is determined. To calculate the post-announcement EPS forecast, for each continuing analyst, this study uses the first post-announcement EPS forecast of an index inclusion. From these individual analysts’ forecasts, the median post-announcement EPS forecast is determined. This study calculates the forecast changes by subtracting the pre-announcement EPS forecast from the post-announcement EPS forecast. If the value is larger than or equal to zero the stock is assigned to the upwards revision of earnings forecast group. If the value is less than zero, it is assigned to the downwards revision of earnings forecast group.

Table 5 shows the mean cumulative abnormal returns for 29 upwards earnings forecast revision stocks and 30 downwards earnings forecast revision stocks during various event periods. Figure 3 shows the trend of the mean cumulative abnormal returns for the two subsamples of added stocks during the event-period. The dashed line represents downwards earnings forecast revision stocks and the solid line represents upwards earnings forecast revision stocks. Downwards revised earnings forecast stocks earn a significant abnormal return of 1.11 % on the announcement day. Companies undergoing upwards earnings forecast revisions also show a significant positive return on the announcement day, but with a lower magnitude (0.95%). The CARs for the other four event periods are all positive and significant. The two-sample t-statistics indicate the difference in CARs between upwards and downwards earnings forecast revision stocks. The CARs for the upwards earnings forecast revision stocks are considerably greater than those for the downwards earnings forecast revision stocks for the event periods, day 1 to day 10 and day 1 to day 15. Investors can benefit by purchasing upwards earnings forecast revision stocks.

Figure 2： Mean Cumulative Abnormal Returns for Large-Scale and Small-Scale Stocks Added to the Nikkei 225, 1991- 2008.

0.14 CAR chart for a (-10, 30) window 0.12

0.1

0.08

0.06 CAR 0.04

0.02

0 -10 -5 0 5 10 15 20 25 30 -0.02 event window This figure shows the trend of mean cumulative abnormal returns for 44 large-scale added stocks and 44 small-scale added stocks in the event-period. The dashed line represents the small-scale stocks and the solid line depicts the large-scale stocks.

Price Responses Displayed by Stocks Deleted from the Nikkei 225

Sufficient price data is available to conduct analysis for 51 stocks deleted from the Nikkei 225 Index.

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Table 2 (Panel B) and Table 3 (Panel B) list the results for the deletions sample. Figure 4 shows the trend of the mean cumulative abnormal returns of deleted stocks during the event-period. This study makes three observations. First, the abnormal returns during the pre-announcement period are significantly positive at the 1% and 10% levels on four days (day -9, -7, -4, -3), providing no evidence of market anticipation.

Table 4： Analysis of CARs of Category 1: Large-scale and Small-scale Stocks Added to the Nikkei 225 Index, 1991- 2008.

Event Period

1 2 3 4 5 6 7 8

Subsample N -10 to -1 0 1 to 5 1 to 10 1 to 15 1 to 30 -10 to 15 -10 to 30

Large-scale 44 0.28% 2.73%*** 5.73%*** 2.62% 1.11% 2.49% 4.12%* 5.5%**

(0.19) (3.96) (3.74) (1.56) (0.70) (1.15) (1.93) (2.01)

Small-scale 44 1.33% 0.88%** 5.12%*** 5.43%*** 4.93%*** 3.31% 7.14%*** 5.52%**

(1.28) (2.30) (3.97) (3.46) (2.90) (1.43) (3.36) (1.97)

Pairwise t-test

Difference -1.05% 1.85%** 0.61% -2.81% -3.82% -0.82% -3.02% -0.02%

p-value 0.562 0.022 0.761 0.225 0.102 0.796 0.319 0.997 This table reports market-adjusted mean cumulative abnormal returns (CARs). The CARs are calculated in various events periods for “Large-scale” and “Small-scale” added stocks from 1991 to 2008. Each cell reports the average CAR for the respective event periods. Day 0 denotes the announcement day. T statistics are reported in parentheses. ***, **, and* indicate significant at 1, 5 and 10 percent levels respectively.

Figure 3： Mean Cumulative Abnormal Returns for Upwards and Downwards Earnings Forecast Revision Stocks Added to the Nikkei 225, 1991- 2008.

0.1 CAR chart for a (-10, 30) window

0.08

0.06

0.04 CAR

0.02

-0.02 event window This figure shows the trend of the mean cumulative abnormal returns for 29 upwards earnings forecast revision stocks and 30 downwards earnings forecast revision stocks during the event-period from 1991 to 2008. The white line represents downwards earnings forecast revision stocks and the black line represents upwards earnings forecast revision stocks.

Second, as with added stocks, a growing announcement reaction, albeit negative, is observed to deleted stocks on the announcement day. The abnormal return is -13.13% which is significantly negative at the 1% level. Third, abnormal returns during the post-announcement period are still significantly negative from days 1 to day 3. However, after day 3, the deleted firms experience a significantly positive abnormal return of 4.20% with a t-value of 4.761 on day 4. Abnormal returns are sometimes positive and sometimes

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negative during the post-event period. The mean cumulative abnormal return for 51 deleted stocks is not significantly reversed in the event period. But from day 2 to day 48, it is fully reversal. The result suggests that the price response of deleted firms in the Nikkei 225 Index is consistent with the price pressure hypothesis. This study has confirmed the price behavior of firms deleted from the Nikkei 225 Index that are not reported in the study of Okada et al. (2006).

Table 5： Analysis of CARs of Category 2: Upwards and Downwards Earnings Forecast Revision Stocks Added to the Nikkei 225 Index, 1991-2008.

Event Period

1 2 3 4 5 6 7 8

Subsample N -10 to -1 0 1 to 5 1 to 10 1 to 15 1 to 30 -10 to 15 -10 to 30

Up 29 0.70% 0.95%** 5.23%*** 5.65%*** 5.19%*** 2.78% 6.83%*** 4.42%

(0.67) (2.28) (3.40) (3.78) (2.74) (1.15) (2.93) (1.54)

Down 30 1.17% 1.11%** 3.13% 0.56% -0.80% -0.80% 1.46% 1.45%

(0.72) (2.07) (1.66) (0.23) (-0.34) (-0.27) (0.52) (0.39)

Pairwise t-test

Difference -0.47% -0.16% 2.10% 5.09%* 5.99%* 3.58% 5.37% 2.97%

p-value 0.807 0.807 0.393 0.081 0.055 0.368 0.150 0.530 This table reports market-adjusted mean cumulative abnormal returns (CARs). The CARs are calculated in various event periods for “Upwards” earnings forecast revision and “Downwards” earnings forecast revision added stocks from 1991 to 2008. Each cell reports the average CAR for the respective event periods. Day 0 denotes the announcement day. T statistics are reported in parentheses. ***, **, and* indicate significant at 1, 5 and 10 percent levels respectively.

Price Responses of Large-Scale and Small-Scale Stocks Deleted from Nikkei 225

There are 51 deleted stocks in Nikkei 225 Index for which sufficient price data is available to examine the price change responses. This study classifies 26 stocks as large-scale deleted stocks and 25 as small-scale deleted stocks. Table 6 shows the mean cumulative abnormal returns for large-scale deleted stocks and small-scale deleted stocks in various event periods. Figure 5 shows the trend of mean cumulative abnormal returns for the two subsamples of deleted stocks during the event-period. The dashed line represents the small-scale deleted stocks and the solid line depicts the large-scale deleted stocks.

Figure 4： Mean Cumulative Abnormal Returns for Stocks Deleted from the Nikkei 225 Index, 1991- 2008.

0.1 CAR chart for a (-10, 30) window 0.05 0 -0.05 -10 -5 0 5 10 15 20 25 30 -0.1 -0.15 CAR -0.2 -0.25 -0.3 event window -0.35

This figure shows the trend of the mean cumulative abnormal returns for 51 deleted stocks during the event-period from 1991 to 2008.

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Large-scale and small-scale deleted stocks respectively experience significant negative CARs in the six event periods. The t-statistics indicate the difference in CARs between large-scale and small-scale deleted stocks. The CARs differ markedly among the four event periods (days -10 to -1, day 0, days -10 to 15, days -10 to 30). The results reveal that small-scale deleted stocks have larger price responses.

Figure 5：Mean Cumulative Abnormal Returns for Large-Scale and Small-Scale Stocks Deleted from the Nikkei 225 Index, 1991- 2008.

CAR chart for a (-10, 30) window

0.1

0 -10 -5 0 5 10 15 20 25 30

-0.1

CAR -0.2

-0.3

-0.4 event window -0.5

This figure shows the trend of mean cumulative abnormal returns for 26 large-scale deleted stocks and 25 small-scale deleted stocks during the event-period from 1991 to 2008. The dashed line represents the small-scale deleted stocks and the solid line depicts the large-scale deleted stocks.

Table 6： Analysis of CARs of Category 1: Large-Scale and Small-Scale Stocks Deleted from the Nikkei 225 Index, 1991- 2008.

Event Period

1 2 3 4 5 6 7 8

Subsample N -10 to -1 0 1 to 5 1 to 10 1 to 15 1 to 30 -10 to 15 -10 to 30

Large-scale 26 -0.10% -6.70%*** -8.10%*** -8.00%*** -8.20%*** -5.60% -15.0%*** -12.4%***

(-0.05) (-3.06) (-4.19) (-3.13) (-2.57) (-1.63) (-5.00) (-4.13)

Small-scale 25 6.70%*** -19.90%*** -24.8%* -24.10% -26.70%* -25.40%* -39.9%*** -38.5%***

(2.62) (-8.33) (-1.70) (-1.67) (-1.88) (-1.81) (-3.34) (-3.26)

Pairwise t-test

Difference -6.80%** 13.20%*** 16.70% 16.10% 18.50% 19.80% 24.90%* 26.10%**

p-value 0.046 0.000 0.267 0.280 0.215 0.183 0.053 0.041 This table reports market-adjusted mean cumulative abnormal returns (CARs). The CARs are calculated in various event periods for “Large-scale” and “Small-scale” deleted stocks from 1991 to 2008. Each cell reports the average CAR for the respective event periods. Day 0 denotes the announcement day. T statistics are reported in parentheses. ***, **, and* indicate significant at 1, 5 and 10 percent levels respectively.

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CONCLUSIONS

This study uses an event study methodology to examine the price changes of stocks either added to or removed from the Nikkei 225 Index during the periods from September 1991 to March 2008. This study demonstrates that prices of stocks added to and deleted from the Nikkei 225 Index respectively fluctuate accordingly on the announcement days. These price trends then reverse during the post-announcement period. These results are consistent with the price pressure hypothesis.

This study also categorizes the composite stocks depending on the market value and upwards and downwards analyst earnings forecast revisions to explore the price reactions of different types of composite stocks. By classifying the composite stocks into two categories, this study finds that small-scale stocks exhibit larger price responses than large-scale stocks. In addition, the results show that newly added stocks with upward revised earnings forecasts earn higher abnormal returns than those with downward revised earnings forecasts during the post-announcement period. These results imply that investors can take advantage of these empirical results by purchasing stocks with upward earnings forecast revisions which are newly added to the Nikkei 225 Index. In this paper, I only consider price response to associated changes in the Nikkei 225 composition. A remaining question is if analysts that more accurately forecast earnings have a different influence on the market. This question will be addressed in future research

REFERENCES

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Chen, H., Noronha, G. & Singal, V (2004) “The Price Response to S&P 500 Index Additions and Deletions: Evidence of Asymmetry and A New Explanation,” Journal of Finance, vol. 59(4), August, p. 1901-1929.

Chen, H., Noronha, G. & Singal, V (2006) “S&P 500 Index Changes and Investor Awareness,” Journal of Investment Management, vol. 4(2), p. 23-37.

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Hanaeda, H. and Serita, T (2003) “Price and Volume Effects Associated with a Change in the Nikkei 225 List: New Evidence from the Big Change on April 2000,” International Finance Review, vol. 4, p. 199-225.

Harris, L. & Gurel, E (1986) “Price and Volume Effects Associated with Changes in the S&P 500 List: New Evidence for the Existence of Price Pressures,” Journal of Finance, vol. 41(4), September, p. 815-829.

Hedge, S. P. and McDermott, J. B (2003) “The Liquidity Effects of Revisions to the S&P 500 Index: an Empirical Analysis,” Journal of Financial Markets, vol. 6(3), May, p. 413-459.

Jain, P. C (1987) “The effect on stock price from inclusion in or exclusion from the S&P 500,” Financial Analysts Journal, vol. 43(1), January – February, p. 58–65.

Kaul, A., Mehrotra, V. & Morck, R (2000) “Demand Curves for Stocks Do Slope Down: New Evidence from an Index Weights Adjustment,” Journal of Finance, vol. 55(2), April, p. 893-912.

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Liu, S (2000) “Changes in the Nikkei 500: New Evidence for Downward Sloping Demand Curves for Stocks,” International Review of Finance, vol. 1(4), December, p. 245–267.

Okada, K., Isagawa, N. & Fujiwara, K (2006) “Addition to the Nikkei 225 Index and Japanese market response: Temporary demand effect of index arbitrageurs,”Pacific-Basin Finance Journal, vol. 14(4), September, p. 395–409.

Shankar, S. G. & Miller, J. M (2006) “Market Reaction to Changes in the S&P SmallCap 600 Index,” Financial Review, vol. 41(3), August, p. 339-360.

Shleifer, A (1986) “Do Demand Curves for Stocks Slope Down?,” Journal of Finance, vol. 41(3), July, p. 579-590.

Shu, P. G., Yeh, Y. H. and Huang, Y. C (2004) “Stock Price and Trading Volume Effects Associated with Changes in the MSCI Free Indices: Evidence from Taiwanese Firms Added to and Deleted from the Indices,” Review of Pacific Basin Financial Markets and Policies, vol. 7(4), p. 471-491.

Wurgler, J. and Zhuravskaya, E (2002) “Does Arbitrage Flatten Demand Curves for Stocks?, “Journal of Business, vol.75(4), October, p. 583-608.

BIOGRAPHY

Chia-Jung Tu works in the Department of Banking and Finance, Kainan University . Chia-Jung can be reached at Tel: 886-3-3412500#7913, fax: 886-3-3412228, e-mail:[email protected] No.1 Kainan Road, Luzhu Shiang, Taoyuan 33857, Taiwan

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THE PRICE OF STOCKS IN LATIN AMERICAN FINANCIAL MARKETS: AN EMPIRICAL APPLICATION OF THE OHLSON MODEL Pedro Martínez, Instituto Tecnológico y Estudios Superiores de Monterrey Diego Prior, Universidad Autónoma de Barcelona Josep Rialp, Universidad Autónoma de Barcelona

ABSTRACT

The emergence of the Latin American market and its growing importance attract global investors to this region with an eye on profit opportunities. This attraction demands a reliable instrument for the calculation of future stock prices of regional companies. This study examined the reliability and validity of the Ohlson Model to predict Latin American stock prices through an empirical application of a panel data analysis of 1,112 companies from this region with data from 2002 to 2009. The findings identified the countries in Latin America where the model can be used successfully.

JEL: G12

KEYWORDS: Ohlson Model, Latin American, Stock Prices

INTRODUCTION

Calculating the value of a firm is of paramount importance; identifying and defining the broad spectrum of factors that influence the prices of stock has been a challenge for the scientific community. The purpose of this paper was to test a model based on accounting information to estimate stock prices for Latin American Markets. As stated in the work, Return to the Fundamentals by Penman (1992) “How does one assess how much to pay for a stock and what is the role of accounting in that assessment?”

The Ohlson Model determines the market value of a company´s stock from accounting data (Ohlson, 1995), such as book value and discounted future dividends. This model has attracted considerable attention (Ota, 2002) and has had considerable impact on the literature in recent years (Larrán & Piñero, 2005). It has an advantage over other models in that the accounting information is available for all the listed companies.

The importance of proposing and answering these questions resides in regional economic development potential. The United Nations (2011), through its statistic Division, estimated the combined midyear population of 2009 for Latin America and the Caribbean was 583.5 million people, of an estimated worldwide population of 6.82 billon. This implies that 8.6% of the world population lives in the Latin America region. Clearly Latin America is a region with great development potential.

Even though a great deal of research has been done using the Ohlson Model for the United States, Western European and Asian countries, no such exists concerning Latin America. There are two publications that have used Ohlson in México, “Value relevance of the Ohlson Model with Mexican data” (Durán, Valdés & Valencia, 2007) and “Ohlson Model by Panel Cointegration with Mexican Data” (Valdés & Durán, 2010). A research gap existed in the literature that offers the opportunity to use Latin American data to probe the validity and applicability of the Ohlson Model to stock markets from this region. This study answers the following research question: Can the Ohlson Model be used in all countries of Latin America?

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The importance of this question resides in the relation between growth and financial structure presented in the work of Greenwood & Jovanovic (1990). These authors demonstrated that in mature economies financial structures were well developed which facilitated the income distribution among people. This is not the case in Latin America, however, as the financial structures are not yet fully developed, and therefore income is not well distributed among the people.

According to a report prepared by the staff of the Infrastructure and Financial Market Division (Wright, Chrisney & Vives, 1995) of the Inter-American Development Bank, there are great differences in domestic financial systems in the Latin American region, which have different economic, social, and political development strategies, highlighting important differences. This report states that the Inter- American Development Bank has been working for several years in the banking system of the region, but recommends that the bank focus on non-bank financial issues, such as the capital markets, with the inclusion of primary and secondary markets. It states that capital markets of the region are poorly developed and have liquidity and transparency problems, which makes them less attractive for domestic investors. This reduces domestic investment in favor of consumption or offshore investments. It is important, therefore, for shareholders and potential investors in the stock market of the countries of Latin America to know if the differences are a major influence in the way financial and operative results affect the price of stocks. In a broader spectrum, it is important for investors to know if there is a model that can be applied to determine the stock value for a specific market for the countries in this region.

Before the Ohlson Model was developed, there were many attempts to calculate the real value of stocks. Some, in accounting terms by simply subtracting liabilities from the assets, or technical analysis based on a graphical interpretation. A more complex method was fundamental analysis based on discounted cash flows. All of these methods have their application, but as Bernard (1995) stated, “the Ohlson Model (Ohlson, 1995) and the Feltham-Ohlson Model (Feltham-Ohlson,1995) stand among the most important developments in capital markets research in the last several years” and could be the foundation for very important research between firm value and accounting information. Ota (2001) argued that these studies have drawn a great deal of attention in the last few years. Many publications and empirical tests appeared after the publication in 1995 of both the Ohlson and the Ohlson-Feltham Models.

The Ohlson Model has proven to be a powerful tool in determining the value of a stock from available accounting information. It is therefore important to test the model in different countries. Globalization has linked the economies of countries in a way never before possible, but throughout history there has been a natural tendency to form economic groups to achieve a competitive synergy. As the European community has created an economic block, Latin America is trying to integrate into an economic region, and given the fact that there are new emerging economies in the region with growth potential, it would appear to be an interesting case for study.

In order to apply the Ohlson Model to the stock markets from Latin America, the Osiris Data base was used. First a pooled regression was completed and compared with the panel data analysis for several countries from this region from 2002 to 2009. This was done with fixed and random effects. According to the model, it was expected that the price for a stock would be a function of the book value and the abnormal earnings, both with a positive correlation. That is, the more book value and abnormal earnings a company has, the greater the market price of the stock should be. Abnormal earnings are those earnings which remain after subtracting the financial cost for the use of capital at a risk free rate. This should have been true for the model that included all countries or for individual counties. If it was not true, the failure could be attributed to institutional differences that affected those countries and rendered the model incapable of estimating the price of the stock with statistical certainty.

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These differences can be explained due to the fact that not all countries have a developed stock market that acts on the basis of the free market without government intervention. Moreover, this model is based on accounting information, so in order for this model to work, standardized accounting data reporting should be well regulated by a centralized authority. All changes in assets and liabilities should pass through the income statement. If a company´s financial statements do not conform to this standard, the model financial data cannot estimate the price of the stock due to accounting distortions. It was expected that the Ohlson Model would work for most Latin American stock markets, with the exception of Venezuela.

The reminder of this paper consist of the Literature Review, which follows immediately, the method used, followed by the results obtained, and ends with the Conclusions, Limitations and suggestions for Future Lines of Research.

LITERATURE REVIEW

In 1932 Viner argued, from an economic point of view, the importance of cost and how equilibrium and rational behavior affect the price of goods and thereby set the basis for price theory. It must not be overlooked, as Milton Friedman stated in his “Price Theory” (2007), that the allocation of resources among different users sets the price of one item relative to another. This empirical generalization is at least two centuries old. From this theory it can be inferred that there are great allocations of resources in the stock markets that can move freely to other markets.

This price theory is important to the scientific community because of the economic implications for the stock market. Market is understood as a means to use, purchase, or sell a resource in order to define its allocation (Friedman, 2007). Other authors have developed models and theories in their efforts to explain asset pricing. For example, Sharpe (1964) developed the Capital Asset Pricing Model (CAPM), Ross (1976) offered the Asset Pricing Theory (APT), and Wei (1988) made an effort to unify these two. The first takes into consideration the risk free rate plus a premium for the specific beta risk, the second takes into account the arbitrage of the market.

There are other models based on dividends and on cash flow. Models that use dividends to calculate the intrinsic value of a stock have the difficulty of predicting future dividends and the rate at which these expected dividends must be discounted. This rate has to do with the firm’s risk (Penman, 1992). There is also a discretionary component to the dividends, but not to accounting numbers.

Another well accepted method is based on discounted future cash flows (DCF’s) that uses cash flows instead of earnings. This popular method comes as a solution to the intrinsic distrust in accounting information (Penman, 1992). It serves in instances where cash flows are real and accounting earning may not be so. This is not so, however, as free cash flows are distributions of wealth instead of the creation of wealth; they do not aggregate value to the stock. This is the main drawback for this commonly used method. As described by Penman (1992), cash flows can come from changes in cash, capital contributions, dividends paid, and net borrowings. Moreover, cash flows can also be affected by changes in accounts receivable, inventories, plant, equipment, and other assets, and to complicate matters, there is also the effect of depreciation, which has an effect on earnings, but not on cash flow.

The determination of the value of a stock from available information is known as fundamental analysis, and according to Penman (1992) was the primary tool for research in investment analysis by the end of the 70´s. Since this period, however, research has been redirected away from fundamental analysis and from accounting measurement theory, for the lack of theoretical foundations. This shift has turned research towards the study of price; that is, towards the technical analysis.

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According to Walker (1997), there was an open rejection to the income measurement perspective, and three major lines of research became popular: market-based accounting, information economics, and positive accounting theory. The Ohlson Model uses market based accounting basic principle of using income measurement theory, where there is a relation between accounting numbers and stock market valuation.

Ohlson´s work has caused academic researchers to return to the fundamental analysis perspective (Walker, 1997). The Ohlson Model can be regarded as a breakthrough, with price based on expected future earnings (Penman, 1992), and with the condition of the use of clean surplus accounting. That is, that all the changes in equity are due to dividends, retained earnings, and capital contributions and must go through the income statements.

The Ohlson Model is presented by James Ohlson in “Earnings, Book Value and Equity Valuation” (1995), and later in “Valuation and Clean Surplus Accounting for Operating and Financial Activities” by Feltham and Ohlson (1995). The models developed in both works relate accounting information to firm value. Basically they assumed the value of a stock can be calculated from the book value and the net present value of the abnormal earnings; that is, the earnings a company makes above the cost of the money used to make them, discounted at a risk free rate, in an accounting system that is based on clean surplus. This means that all changes in assets and liabilities unrelated to dividends must pass through the income statement (Ohlson, 1995).

The Ohlson Model takes into consideration the work of Franco Modigliani and Merton Miller (1958); “The Cost of Capital, Corporation Finance and the Theory of Investment”, in which the irrelevancy proposition stated that stock price is unrelated to observed dividends. Dividends reduce value dollar for dollar, and dividends reduce future value (Miller & Modigliani, 1961). The work of Peasnell (1981) argued that there is a relationship between discounted cash flow methods and accounting performance measures, such as the abnormal earnings used in Ohlson´s Model.

According to Walker (1997), the principal advantages of the Ohlson Model are that this model tries to explain value directly and refocuses on fundamental valuation, as seen by Penman (1992). This model has provided a solid theoretical framework for market-based studies through the clean surplus approach. This approach indicated that market value will be equal to book value plus the discounted present value of expected abnormal profits which tend to zero in a competitive market unless their innovation is permanent. It also takes into consideration the impact of retained earnings in future earnings.

The literature currently contains several important empirical research studies based on the Ohlson Model. The study written by Ota (2001) had a very clear mathematical development of the Ohlson Model and showed great concern with the difficulty in estimating the factor for other information variables that affect the future performance of a firm. A study by Duran et. al. (2007) attempted to validate the application of the model to Mexico´s companies, but used net income instead of abnormal earnings in its testing.

Lundholm (1995) provides insight into the Ohlson and Feltham-Ohlson Models by describing the model, how it worked, and by answering several other researchers’ questions such as one by Ota (2001) that concerned the “other information” that affects the price of the stock and the difficulties that it carries when it is incorporated into the Ohlson Model. Ota (2001) concluded that the model has been tested empirically and gives rigor to these tests. It is a good representation of the valuation method from accounting. Larrán & Piñero (2005) made an interesting study for a case involving dirty surplus, which is any variation in net equity due to any cause except those that affect the retained earnings (Larrán & Piñero, 2005). Callen & Segal (2005) showed the Feltham-Ohlson Model can be a good estimator of price, when other information factors are added.

The International Journal of Business and Finance Research ♦ VOLUME 6 ♦ NUMBER 4 ♦ 2012

Two research studies have used Mexican data applying the Ohlson Model. Duran, Valdés & Valencia, (2007) provide empirical evidence of the ability of the Mexican accounting system to predict stock prices using the Ohlson Model from 166 companies, from 1991 to 2003, with panel data analysis utilizing book value and earnings. Their results were statistically significant and therefore relevant. The other study (Valdés & Durán, 2010), was a panel analysis by economic sector (food & beverage, commercial and construction) on a quarterly basis from 1997 to 2008, also using the Ohlson model. The study proved to be relevant only in the commercial and the food & beverages sectors.

Another study by Giner & Íñiguez (2006) corroborated the predictive ability of the Ohlson (1995) and Feltham Ohlson (1995) Models from future earnings. For their study, they took non-financial companies from the Madrid stock market from 1991 to 1999, using book value and earnings. The simplifications they used were the same as those used by Duran, et. al. (2007), using earnings instead of abnormal earnings and discarding financial companies without a proper justification.

METHODOLOGY

In order to apply the Ohlson Model to the stock markets from Latin America, the Osiris Data base was used. First a Pooled Regression was made and compared with the panel data analysis for several countries from this region from 2002 to 2009. We expected the model to be statistically valid to predict the value of stock prices in most of countries; with the exception of Venezuela. More than 23,000 observations for 2,912 listed companies in 34 countries in Latin America were used.

Market value depends on future expected dividends, under a clean surplus accounting system, and has positive correlation with abnormal earnings; that is, the earnings above the cost of equity at a risk free rate. The Ohlson Model rests, in the neoclassical view, on the assumption that the price of a stock is a function of present value of the expected dividends discounted at a risk free rate.

[ ] = (1) 퐸푡 푑푡+휏 ∞ 휏 푡 휏=1 푃 ∑ �1+푟푓� Where

푡 d푃t=푤푎푠 net dividends푡ℎ푒 푝푟푖푐푒 paid표푓 푡 ℎat푒 date푓푖푟푚 t and푠 푒푞푢푖푡푦 푎푡 푑푎푡푒 푡

rf= the risk free rate

[ ]

푡 푡+휏 If퐸 x푑t=earnings푡ℎ푒 푒푥푝푒푐푡푒푑 from t-1 t푑푖푣푖푑푒푛푑푒푠o t and yt= net푎푡 book푡푖푚푒 value푡 at t

Then, in using the Modigliani & Miller (1958) principle yt-1 = yt+dt-xt

Where yt-1 is the net book value in t-1. If a clean surplus accounting is present, the abnormal return could be expressed as:

(2) 푎 푡 푡 푓 푡−1 And푥 ≡ the푥 price− 푟 ∙of푦 the stock could be expressed as:

P. Martinez et al | IJBFR ♦ Vol. 6 ♦ No. 4 ♦ 2012

[ ] = + 푎 (3) 퐸푡 푥푡+휏 ∞ 휏 푡 푡 휏=1 푃 푦 ∑ �1+푟푓� So that after some algebraic manipulation the linear expression was:

= + + (4) 푎 푃Adopting푡 푦푡 the훼1 푥Ohlson푡 훼2 M훾푡odel used by Collins et al. (1999), and taking the model:

= + + with a few notation changes; that is, = and = 푎 푎 푎 The푃푡 sub푦푡 index훼1푥 i푡 for each훼2훾푡 company, we arrived at: 푦푡 퐵푉푡 푥푡 퐴퐸푡

= + + + + (5) 푎 푖푡 0 1 푖푡 2 3 푖푡 푖푡 푃For this훼 model훼 퐵푉, was훼 퐴퐸taken푖푡 from훼 훾 the 휀“MarketCap0atclosingdatethUSD” variable, which was the total value of a company at the closing. The Book Value was taken from the Osiris data base from the variable, “ShareholdersFundsthUSD”푃푖푡 , and the Abnormal Earnings was estimated by the variable “NetIncomethUSD” as was seen in Duran, et al, (2007).퐵푉 푖푡The term , which stands for other information that affects the price, was neglected due to the difficulties in estimating this parameter, (Ota, 2001). These variables were expressed in thousands of US dollars. 훾푖푡

With this model, a panel data analysis, or cross sectional time series was made because of the considerable advantages that it offered (OĆonnell, 2007), such as the control of unobservable firm- specific effects that are difficult to measure. OĆonnell (2007) also points out the reduction in the impact of omitted variables due to correlation, and the fact that it was useful to determine whether a particular parameter varies over time or country. The panel data analysis had the advantage of allowing the identification of countries where accounting conservatism was different (OĆonnell, 2007). This advantage was used to analyze what countries had a restriction for the application of the Ohlson Model.

The market capitalization variable was used as the company market price for the Ohlson Model. Shareholders’ Funds were used as book value. Net income was used as an approximation of abnormal earnings (Duran et al., 2007)). The use of US dollars in every country gives the advantage of easy comparability, and the cost of money would be the same for all the countries.

Observations with no market capitalization or negative market capitalization values were eliminated from the study, the latter category because it contained the companies that were technically bankrupt. Countries that had less than 5 companies left after this adjustment were also discarded. With this elimination, only 13 countries remained, with a total of 1,112 companies, as shown in Table 1, for a total of 8,896 observations.

The Variables used for this analysis were: “MarketCap0atclosingdatethUSD”, the dependent variable is the market capitalization value at the end of each year for every company taken into consideration in this study. All values are in thousands of US Dollars. “ShareholdersFundsthUSD” was an independent variables; “NetIncomethUSD” was the other independent variable and measures net income. For the purpose of this study, it will be considered as the abnormal earnings, Duran, et al (2007)

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Table 1: Observations by Country

Counties 2002 2003 2004 2005 2006 2007 2008 2009 Total ARGENTINA 60 60 60 60 60 60 60 60 480 BERMUDA 312 312 312 312 312 312 312 312 2496 BRAZIL 212 212 212 212 212 212 212 212 1696 CAYMAN ISLANDS 197 197 197 197 197 197 197 197 1576 CHILE 147 147 147 147 147 147 147 147 1176 COLOMBIA 44 44 44 44 44 44 44 44 352 EL SALVADOR 6 6 6 6 6 6 6 6 48 HONDURAS 8 8 8 8 8 8 8 8 64 JAMAICA 12 12 12 12 12 12 12 12 96 MEXICO 45 45 45 45 45 45 45 45 360 PANAMA 9 9 9 9 9 9 9 9 72 PERU 53 53 53 53 53 53 53 53 424 VENEZUELA 7 7 7 7 7 7 7 7 56 Total 1,112 1,112 1,112 1,112 1,112 1,112 1,112 1,112 8,896 This Table shows the observations for each country from 2002 to 2009 after the elimination of the companies with zero or negative market capitalization value or had less than 5 observations.

The first analysis was a pooled OLS (Ordinary Least Squares) regression, which allows a simple analysis. Pooled OLS may not be appropriate if the parameters differ over time and/or across firms due to heterogeneity (O´Connell, 2007). For this reason we use panel data estimation and compared the results with pooled OLS regression results. As it was important to determine if the Ohlson Model was valid for the prices in the stock markets from the region through several years, panel data seemed appropriate. To establish validity, a panel analysis with random effect was tested, to determine if this analysis was better than the pooled regression. The test used was formulated by Breusch and Pagan, and is known as the Lagrangian Multiplier test for random effects. The panel analysis with fixed effects was tested and compared with the F value. If both of these analyses are better than the pooled regression, a Hausman test would be used to determine which of the two was better.

RESULTS

This pooled Regression OLS model was: = + + (6)

Where i was the company and t was time (year).푌푖푡 The훼 results훽1푋1 are푖푡 presented푒푖푡 in Table 2. From Table 2 with this pooled regression OLS, the R square value of the determination coefficient was 0.6931, and the overall F test was displayed with a 0.0000 value, which allowed rejection of the null hypothesis, and therefore it could be concluded that the model was valid. The p values for the t test for the individual coefficients, the constant, and the independent values were shown to be statistically significant, with values of 0.0000 in each case. All the coefficients were positive, and this was consistent with the theoretical expected values for the Ohlson Model. Therefore, the effect of net income and book value (Shareholder) had a positive and significant effect on the price of the stock (MarketCap), as expected.

The fitted model would be: MarketCAP= 391,529+ .6502Shareholder+ 2.000 NetIncome

In the Ohlson Model terms: Price=391,529+ .6502 Bookvalue+ 2.000 Abnormal Earnings

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Table 2: Pooled Regression OLS

Number of Observations Prob> F R-Square Adj. R-Square 5,376 0.0000 0.6931 0.6929

MarketCap Coef. 95% Conf. Interval Sharehoder 0.6502*** 0.6150 0.6854 NetIncome 2.000*** 1.819 2.182 Const. 391,529*** 335,341 447,717 This table shows the result of the Pooled Regression OLS, the statistical significance of the Model, the determination coefficients and the coefficient values.***,**,* indicate the significance at 1, 5, 10 percent respectively.

The cross sectional time series, also known as panel data analysis, consisted of the observation on the i units (companies) over t periods of time t (years). This allowed control of each company. In this second step, the Generalized Least Square (GLS) random effects estimator was analyzed so that the model assumed that each unit (company) had a different constant in order that the previous equation could be expressed as:

= + + (7)

Where푌푖푡 훼 푖 =훽1푋+1푖푡 푒 this푖푡 equation could be rewritten as: = + + (8)

The results훼푖 are훼 presented푢1 in Table 3. The results show the 푌R푖푡 square훼푖 within,훽1푋1푖푡 that푢 is푖+, 푒the푖푡 explained variation within companies, was less predictable with this model, which was 0.5064. This can be defined as the squared correlation between deviations of values from the company mean ( ), and deviations and predicted values from companies mean predicted values. 푌푖푡 푦푖푡 − 푦�푖 The R square between was the variation explained among the companies. The results show the model did a very good job of explaining these variations with a overall R Square of almost 0.70; this was a very good overall value.

The Chi square p value was 0.0000; showing that the coefficients were zero and the null hypothesis could be rejected. This gave the statistical validity of the model and showed that both independent variables were positive and statistically significant, which again was consistent with the theory behind the Ohlson Model. The maketCap variable, or price, increased 0.7393 for each additional dollar of shareholder or book value increase, holding net income constant. On the other hand, if the shareholder value remained constant, the market capitalization increased 1.482 dollars for each dollar net income increased, which approximated abnormal earnings. On the lower right side, the standard deviations of the common residuals and the unique residual were shown as 1,428,042 and 1,422,318 respectably. And rho was a fraction of unexplained variance due to difference among companies. 푢푖 푒푖 To determine which of the two previous models, pooled regression or random effects, was better, a statistical probe of the null hypothesis could be performed. If in equation (8) the variance of was zero; that was, if = 0 then there was no difference between equations (6) and (8) or between the two 푖 models. A test formulated2 by Breusch and Pagan, known as the Lagrangian Multiplier test푢 for random effects, was be휎 conducted푢 to make this determination. The result of this test indicate the null hypothesis was rejected, given that the P value of the Chi Square was 0.000. Therefore, the random effects are relevant, and that the random effect model is better that the pooled regression model.

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Table 3: Random-Effect GLS Regression

Number of Number of R-Square Observations Groups Prob> Chi2 Within Between Overall 5,375 819 0.000 0.5064 0.7317 0.6913 MarketCap Coef. 95% Conf. Interval Shareholder 0.7383*** 0.7049 0.7716 Sigma_u 1,428,042 NetIncome 1.482*** 1.306 1.658 Sigma_e 1,422,318 Cont. 376,389 268,552 484,357 Rho 0.5020 This table shows the result of the Random-effect regression, the statistical significance of the model, the R– Square values, the Coefficients of the independent variables, the confidence level and the standard deviation of u, e and the value of the fraction of the variance due to u_i Rho. ***,**,* indicate the significance level at 1,5,10 percent respectively

Another model tested was a panel data analysis with fixed effects. This model assumed the differences within companies were constant, and the intercept for each company should be estimated. This could be accomplished by using a vector of dichotomic variables for each company with the following 푖 model: 푢

= + + (9)

푌From푖푡 훾Table푖 훽 14,푋 1the푖푡 R푢 square푖+푒푖푡 values were very similar to the random effects model, and the p value for the F test was 0.0000. It could be assumed that this was a valid model. The values for Sharehoder and Net Income variables were positive and statistically significant, so it was necessary to determined which model was better, this or the pooled regression model for equation (6). The bottom line of the output shows the null hypothesis that; = =. . . . = = 0, and that the F value was 0.000, which allows rejection of the null hypothesis. This indicates the fixed effect model was better than the pooled regression. 훾1 훾2 훾푖

The next step was to identify which of the last two models was better: the random model or the fixed effect model. To make this determination, an analysis was done of the possible correlation between the individual error and the variables. The random effect model assumed that this correlation was zero, but if and the X variables were somehow correlated, then if u was not included in the model, a shift would occur on the푢1 omitted variable in the X coefficients. 푢1 1 Hausman demonstrated the difference between the fixed and the random coefficients could be used to test the null hypothesis: that u and the X variables were not correlated and therefore the 푓푒 푟푒 random effects model was better. If Ho was rejected, the estimators differ and the fixed model� 훽was− better. 훽 � If the null hypothesis could not be rejected, it 1was preferable to use the random effect model because it was more efficient. The Hausman Test gave a 0.0000 p value for the Chi Square, the null hypothesis was rejected, so the fixed effect model should be used.

Table 4: Fixed-Effect GLS regression

Number of Number of R-Square Observations Groups Prob> Chi2 Within Between Overall 5,375 819 0.0000 0.5080 0.7214 0.6879 MarketCap Coef. 95% Conf. Interval Shareholder 0.8142*** 0.7767 0.8516 Sigma_u 1,665,606 NetIncome 1.143*** 0.9489 1.336 Sigma_e 1,422,318 Cont. 358,778 314,649 402,907 Rho 0.5783 This table shows the result of the Fixed-effect regression, the statistical significance of the model, the R–Square values, the Coefficients of the independent variables, the confidence level and the standard deviation of u, e and the value of the fraction of the variance due to u_i Rho. ***,**,** indicate the significance level at 1,5,10 percent respectively.

P. Martinez et al | IJBFR ♦ Vol. 6 ♦ No. 4 ♦ 2012

A dichotomic variable could be added to the model to see if there were common events for all the companies during a specific year, so that our equation was now:

= + + + (10)

푌To푖푡 see훾 푖if the휂1 Ohlson훽1푋1 푖푡Model푒푖푡 could be applied to each Country, a vector of dichotomic variables was used. Tests were done under the fixed effect scope because of the result of the Hausman test. In Table 5 the results of this analysis was presented. 휂1

In Table 5 El Salvador, Honduras, and Jamaica were omitted because of the lack of observations or collinearity problems. Furthermore, from the F test p values, the Ohlson Model did not explain the stock prices for the cases of Venezuela and Argentina; also the R square value for Venezuela was low with 1% of the variance being explained by the model. In these two countries the independent variables were not significant. In the case of Argentina the price of the stock had a negative factor in the shareholder variable, and in the case of Venezuela, the NetIncome variable was also, although none were statistically significant in both cases.

Table 5: Panel Data Analysis with Fixed effect by country

Country Constant Shareholder NetIncome R² F Argentina 823,033*** -0.0505 1.2358 0.7319 1.03 Bermuda 198,927*** 0.8419*** 1.5339*** 0.7045 589.23*** Brazil 508,540** 0.8801*** 0.4197 0.7670 295.20*** Cayman Islands 34,709 1.3938*** 3.0931*** 0.4925 232.72*** Chile -110,718 1.5961*** 3.0733*** 0.8069 305.74*** Colombia -1,616,491* 5.0383*** -21.007** 0.1686 18.680*** Mexico 700,538** .6397*** 5.4558*** 0.6316 84*** Panama -59,122 -1.767 19.6347** 0.7868 10.690** Peru -497,206*** 3.5599*** 1.441 0.7116 101.97*** Venezuela 395,628 0.2255 -1.1718 0.0105 0.9100

This table shows results for the panel data analysis with fixed effect, separated by country, El Salvador, Honduras and Jamaica where eliminated due to collinearly or lack of observations. ***, **,* indicate significance at 1,5,10 percent respectively.

From Table 5, Bermuda, Cayman Islands, Chile, and Mexico had positive and significant values, as expected from the Ohlson Model. In the case of Peru and Brazil, both values were also positive, but in the case of NetIncome variable they were not significant.

Colombia had a statistically good model with a 0.0000 p value for the F test, but the R square factor was 0.1686, which means that it is a poor estimator of the variance. In addition, the value for the net income variable was negative with more than 95% statistical significance, meaning that there had to be a difference that did not allow the Ohlson Model to predict the stock value. As a result, as the NetIncome variable increased, the stock price dropped. In the case of Panama, the p value was lower than 0.05, and the R square was 0.7868, which means that the Ohlson Model gave a good explanation of the variance, but the shareholder variable was negative, although it was not significant.

CONCLUSIONS

The goal of this paper was to test the validity of the Ohlson Model as a price estimator for stock prices in Latin America. Several statistical methods were used. First a Pooled Regression OLS was conducted, followed by a Random and then a Fixed Effect panel data analysis. It was determined that the Fixed Effect was the best, so this was applied to each country through a dichotomic variable by country.

The International Journal of Business and Finance Research ♦ VOLUME 6 ♦ NUMBER 4 ♦ 2012

The Ohlson Model was found to be a good price estimator for stock prices in Bermuda, Cayman Islands, Chile, and Mexico, due to the fact that the variable coefficients were both positive and statistically significant. In the case of Brazil and Peru, both variables were positive, but the coefficient for the NetIncome was not significant. According to the F value, all Ohlson Models were good with p values of 0.0000 except for the case of Panama, which had a p value smaller than 0.05, and Shareholder value, which was negative even though it was not significant. Colombia had a good model with a low R square, and the NetIncome variable was statistically significant but negative, which was contrary to the theory.

In the cases of Argentina and Venezuela, the Ohlson Model did not work due to the fact that the F test showed that models were not statistically correct. For Argentina, the R square value was fair with a value of 0.7319, but the only significant value was that of the constant, and there was a negative value in the shareholder variable. In the case of Venezuela, the Ohlson Model did not work, as expected. The R square value was 0.0105, which was the percentage of variance explained by the model. The F test p value was not significant for all of the variables and the model constant.

We conclude the Ohlson Model is a powerful tool to predict the price of stocks for most Latin American stock markets, with the exception of Venezuela, Argentina, and Colombia. In the case of Brazil, Peru, and Panama the model worked, but at least one of the variables was not statistically significant or had a negative coefficient.

A limitation of this research was the lack of examination of the different regulatory frameworks for each county and institutional differences. Another was the use of the US dollar, which simplified the analysis but could have impacted the results of the Ohlson Model application, due to possible currency devaluation.. Some other limitations of the present study included scale problems, autocorrelation, or heterocedastisity, which could be present. Therefore, a more sophisticated analysis is required. Another limitation was the use of net income as a proxy for abnormal earnings.

A future line of investigation might replicate this analysis by economic sector to determine if there are functional differences by economic sectors that can be detected. In several works, such as Ota (2001) and Duran (2007), it is interesting that they eliminated from their analysis “a priori” the financial sector, without further empirical research.

An additional suggestion for further study would be to study the effect of bull and bear markets in each country of this region, and the relationship to the US and Canadian markets, given that, for geographic reasons, these countries should be viewed as from the same economic block. An important modification to the model can be made by estimating the factor, “Other Information”, with different suppositions to see if the model improves

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BIOGRAPHY

Pedro Antonio Martínez Acosta, part time professor in the Finance Department at the Instituto Tecnológico y de Estudios Superiores de Monterrey, with master’s degrees in: Business Management, Finance, Industrial Engineering and Business Creation. Working on a Ph.D. Thesis at the Autonomous University of Barcelona. Contact: [email protected]

Josep Rialp, Ph.D. is Associate Professor in Marketing and Market Research in the Business Economics Dep. of Autonomous University of Barcelona, Spain. He is author and/or co-author of different books, chapter books or papers in both national and international academic journals such as International Business Review, Advances in International Marketing, Journal of World Business, Regional Studies, Journal of Advertising, and Journal of Small Business Management, among others. His research interests are: SME management and marketing, interorganizational collaboration, knowledge management and research techniques. Dr Josep Rialp also serves as associate editor in Cuadernos de Economía y Dirección de la Empresa as well as ad-hoc reviewer for different academic journals. Contact: [email protected]

Diego Prior Jiménez, Ph.D. Full Time Professor in the Business Economics department at the Universitat Autonoma de Barcelona. Member of the Editorial Committee of several academic journals related with accounting control and financial analysis. Research interests, are the efficiency analysis of organizations and firms’ financial analysis. In the efficiency analysis field, oriented towards the design of models to assess the efficiency of organizations and to design programs to improve their performance. In the financial analysis, my research is oriented towards the comparative benchmark of performance and risk of the European firms in the framework of global competition. Worked on 24 research projects; appearing as principal researcher in 19 of them. I have published 59 articles, 47 of them in Spanish and international academic journals. I have written 13 books and books chapters. I have presented 122 papers in scientific conferences and 17 invited sessions. Participated in 11 doctoral dissertations as supervisor E-mail: [email protected]

The International Journal of Business and Finance Research ♦ VOLUME 6 ♦ NUMBER 4 ♦ 2012

FORECASTING TERM STRUCTURE OF HIBOR SWAP RATES Mei-Mei Kuo, National Taiwan University of Science and Technology Shih-Wen Tai, Lunghwa University of Science and Technology Bing-Huei Lin, National Chung Hsing University

ABSTRACT

To investigate yield curve dynamics, researchers have employed a wide variety of models, including the famous Nelson-Siegel level, slope, and curvature factors, and principal components analysis, among others. In this paper, we decompose the term structure of HIBOR (Hong Kong Interbank Offered Rate) swap rates by means of the Nelson-Siegel factors and principal components analysis, and employ autoregressive and vector autoregressive for ex ante forecasting the yield curve by predicting the dynamic factors and components. We compare the results of a broadly empirical prediction with benchmark models such as random walk and yield levels. Further, we survey the predictability in the shape of the swap yield curve for these models. Our results appear to show that the Nelson-Siegel model with autoregressive process on factor changes is the most efficient model for forecasting HIBOR swap yields.

JEL: E43; E47; C53

KEYWORDS: term structure, Nelson-Siegel model, principal component analysis, HIBOR swap rate

INTRODUCTION

ccurate estimations of the future term structure of interest rates have become a critical issue for financial market participants. Improved methodology for fitting term structure modeling has A been gaining increased attention, but advances in term structure forecasting are relatively rare. Forecasting the term structure plays an important role in derivatives pricing and for risk management hedging purposes. As we know that the arbitrage-free models and equilibrium models are the two popular term structure modeling approaches. The arbitrage-free models (see Ho and Lee, 1986; Hull and White, 1990; Heath, Jarrow, and Morton, 1992) focus on perfectly fitting the term structure at a point in time, notably few literature about forecasting has been developed. The equilibrium models, i.e. affine models (see Vasicek, 1977; Cox, Ingersoll, and Ross, 1985; Duffie and Kan, 1996) focus on modeling the dynamics of the instantaneous rate. The literature on affine models term structure forecasting, such as de Jong (2000) and Dai and Singleton (2000), only consider instantaneous short rates and focus on in-sample fit. Moreover, Duffee (2002) demonstrates disappointing out-of-sample forecasting power for affine models.

In contrast to the arbitrage-free and affine models, Nelson and Siegel (1987, hereafter NS) introduce a parametrically parsimonious model that has the ability to show the shapes of the yield curves: monotonic, humped, and S-shaped. The curve provides a good fit to the cross section of yields in many countries and has become a widely used model among central banks and market participants. In practice, we are not able to detect the spot rates for all periods because of the limited amount of bonds issued in a particular country, especially in emerging markets. However, the NS model overcomes this limitation and captures the entire behavior of the spot rate curve using only three major parameters. In addition to the NS model, principal component analysis (PCA) is another popular technique applied to describe yield curve behavior in a parsimonious manner, notably large empirical tests for PCA have been developed in the literature.

MM. Kuo et al | IJBFR ♦ Vol. 6 ♦ No. 4 ♦ 2012

In emerging markets, government bond markets lack liquidity, so the swap rates become the important proxies for the spot rates for these financial participants. The goal of this paper is to investigate the most efficient model for forecasting term structure of the HIBOR swap rates yield curve and survey the predictability in the shape of the swap yield curve for different models. In this paper, we first decompose the term structure of HIBOR swap rates by means of NS and PCA, and employ autoregressive of order 1 AR(1) and multivariate vector autoregressive VAR(1) models for ex ante forecasting. To examine out-of-sample forecasting performance, we further take first differences for three factors and compare them with benchmark models, such as random walk and yield levels with AR(1) and VAR(1). The model has its best forecasting performance in terms of minimum root mean squared forecast errors (RMSEs). Moreover, we investigate the out-of-sample predictive power of the shape of the swap yield curve, including level, slope, and curvature.

LITERATURE REVIEW

This section summarizes the previous studies using various models to forecast the term structure of interest rates. NS introduce a parsimonious three-component model that has the ability to describe the shapes of the yield curves. The three time-varying parameters may be interpreted as factors. It is well known that the three-component model has competitive in-sample predictions. Despite of NS provide a good cross-sectional interpolation of yields across maturities, the NS model is not well suited to out-of- sample time series forecasts, because these factors do not stay constant over time. To enhance yield curve forecasts, Diebold and Li (2006, hereafter DL) reformulate the parsimonious three-factor yield curve of the NS model. DL proposes and estimates autoregressive models for the factors, and then forecasts the yield curve by forecasting the factors. Taking a dynamic factor approach, DL constructs the term structure of government bond yields by means of level, slope, and curvature factors, as well as macroeconomic variables, such as inflation and the federal funds rate. The DL model with AR(1) factor dynamics at 1-year-ahead horizons turns out to outperform various standard benchmark models like random walk, and the Fama-Bliss (1987) forward rate regression model.

Since DL provides only short-term out-of-sample forecasts (e.g., 1-, 6-, and 12-month-ahead forecasts), Yu and Zivot (2011) extend the DL dynamic forecast model to a wider empirical perspective. These authors evaluate the long-term (e.g., 36- and 60-month-ahead) forecasts of Treasury bonds and corporate yields, and compare the forecasting performances of the one-step (i.e., state space approach) with the two-step (i.e., NS) models. Yu and Zivot (2011) suggest that the simple two-step dynamic NS model retains the long list of robustness checks on out-of-sample forecast accuracy, especially for investment grade bonds at short-term horizons and for high yield bonds at long-term horizons. Meanwhile, the forecasting performance is improved by incorporating macroeconomic variables. Their findings also suggest that the state space model is not necessarily better than the simple NS with AR (1) model.

In addition to extensions of the NS model, principal component analysis (PCA) is another popular technique applied to describe yield curve behavior in a parsimonious manner. A strand of the literature focuses on applying PCA to yield curve decomposition; for example, Litterman and Scheinkman (1991) use PCA to describe US bond returns as mainly determined by three factors: level, slope and curvature. Knez, Litterman, and Scheinkman (1994) provide a four-factor model by observing money market returns, and argue that the additional factor is related to private issuer credit spread. Further, Duffie and Singleton (1997) suggest that the swap contracts comprise default risk, hence arbitrage-free or equilibrium term structure models developed for default-free government bond markets are not applicable to the swap market. They propose a multifactor model for interest rate swap and conclude

The International Journal of Business and Finance Research ♦ VOLUME 6 ♦ NUMBER 4 ♦ 2012

that both credit and liquidity risks impact the US swap spread. Moreover, Blaskowitz and Herwartz (2009) decompose the EURIBOR (European Interbank Offered Rate) swap term structure by means of PCA and apply AR models for adaptive forecasting. These authors find that the PCA/AR model shows additional gains in directional accuracy and forecast value when compared to the benchmark models.

On the other hand, there is little literature focusing on the predictability of the shape of the yield curve; some exceptions are Dolan (1999), Fabozzi, Martellini, and Priaulet (2005), and Diebold and Li (2006). Dolan (1999) supports the notion that the parameters of the yield curve, estimated from the NS model, can be used to predict changes in curvature of the forward curve centered at the three-year maturity; further, Dolan’s empirical evidence advocates that the curvature parameter is correlated closely with the curvature at the four-year spot rate. Fabozzi et al. (2005) use government bond yields and swap rates to estimate autoregressive models for predicting NS level, slope, and curvature dynamics, and conclude that while the level and slope factors are not stationary, the curvature factor seems to be so. Further, Fabozzi et al. (2005) provide out-of-sample predictability for the shape of the yield curve and suggest that changes in level of interest rates are not predictable at the monthly level, but that the slope of yield curve has the best predictive power. The nonstationary processes of factor dynamics compel us to consider the first difference of factor dynamics for the AR and VAR models in this paper.

It is clear that much of the literature focuses on developed countries. However, in emerging markets with underdeveloped government bond markets, the swap curve is more complete than the sovereign yield curve. Swap spreads represent the difference between swap rates and sovereign yields. They reflect the difference in default risk for the financial sector, and have become quite important for emerging markets. Thus, swap spreads represent a data set for examining how both default and liquidity risks influence securities returns. Hong Kong has one of the most open local financial markets in Asia. In addition to its multi-currency and multi-functional financial platform, Hong Kong is the only Chinese Yuan (CNY) bond market outside mainland China. The China factor allows Hong Kong to have a unique competitive advantage that is difficult for other financial centers to replicate.

Huang, Neftci, and Guo (2008) find that the Hong Kong swap curve is richer in information than the sovereign yield curve and the Libor-type interest rates that are the standard measures of market liquidity. The increasing and special importance of the Hong Kong interest rate swap market and the lack of attention paid to forecasting the term structure encourage us to bridge this gap in the literature by forecasting the term structure of swap rates and the predictability in the shape of the swap yield curve using HIBOR (Hong Kong Interbank Offered Rate) swap rates.

The remainder of this paper is organized in the following sections. In Methodology, we introduce a detailed description of the data and the methodology of forecasting the term structure. In Results and Discussion, we proceed to an empirical analysis, including evaluation of out-of-sample forecasting performance. In Conclusions, we set forth our conclusions.

METHODOLOGY

In this paper, we propose an explicitly out-of-sample forecasting perspective to compare a total of 11 models, including the NS, PCA, random walk, and yield level models.

Data

We use the weekly interest rate swap of HIBOR, taken form DATASTREAM over the period April 29 2002 to July 25 2011. The maturities of the swap yields include 1, 2, 3, 4, 5, 7, and 10 years, and the

MM. Kuo et al | IJBFR ♦ Vol. 6 ♦ No. 4 ♦ 2012

total number of observations in each maturity sample is 483. In Table 1, we present descriptive statistics for the HIBOR swap yields. It is clear that the swap yield curve is upward sloping.

Table 1: Descriptive Statistics (swap rates in percent)

Maturity (yr) Mean StD Median Max Min Max Observations 1 2.142 1.524 1.680 0.400 0.310 5.040 483 2 2.533 1.346 2.290 2.170 0.440 5.060 483 3 2.908 1.189 2.860 2.780 0.670 5.110 483 4 3.211 1.071 3.280 1.900 0.980 5.160 483 5 3.452 0.992 3.610 3.780 1.310 5.200 483 7 3.765 0.895 4.000 4.520 1.940 5.590 483 10 4.059 0.865 4.270 3.240 1.870 6.040 483 This table shows descriptive statistics of location and dispersion for weekly data for the period April 29 2002 through July 25 2011.

Model

The models of swap yields we consider are: i. NS with AR(1) factor dynamics (NSAR); ii. NS with VAR(1) factor dynamics (NSVAR); iii. NS with AR(1) factor change dynamics (NSARD); iv. NS with VAR(1) factor change dynamics (NSVARD); v. PCA with AR(1) component dynamics (PCAR); vi. PCA with VAR(1) component dynamics (PCAVAR); vii. PCA with AR(1) component change dynamics (PCARD); viii. PCA with VAR(1) component change dynamics (PCVARD); ix. Random walk (RW); x. AR(1) on yield levels (YDARL); xi. VAR(1) on yield levels (YDVARL). The VAR(1) on yield levels assumes that there are neither integrated nor co-integrated series in the data. We describe these models as follows: i. NSAR The term-structure model introduced by NS and reformulated by DL as a modern three-factor model of level, slope and curvature:

( ) = + + , (1) −휆푡푚 −휆푡푚 1−푒 1−푒 −휆푡푚 where푦푡 푚 denotes훽1푡 훽 2the푡 � yield휆푡푚 at� time훽 3t,푡 and� 휆 푡푚 is the− 푒 maturity� of the bond. denotes the exponential decay rate. The three parameters , , and are interpreted as three latent dynamic factors. The loading 푡 푡 on 푦is 1, which is a constant in all maturities;푚 hence it is interpreted휆 as a level factor or long-term 1푡 2푡 3푡 factor. The loading on 훽is (1훽 훽) , a function that starts at 1, but that decays quickly and 1푡 monotonically훽 to zero when increases−휆푡푚; hence it is interpreted as a slope factor or short-term factor. 2푡 푡 The loading on is 훽(1 − 푒) ⁄휆 푚 , which starts at 0 (not short-term) with a humped shape in the middle; hence, it 푚is− 휆interpreted푡푚 as a− 휆curvature푡푚 factor or medium-term factor. 훽3푡 � − 푒 ⁄휆푡푚 − 푒 � We first use the univariate AR(1) model to estimate the three factors , , and by least squares, period by period, then predict yields through the NS model for forecasting factors as follows: 훽̂1푡 훽̂2푡 훽̂3푡 | ( ) = , | + , | + , | , (2) −휆푡푚 −휆푡푚 1−푒 1−푒 −휆푡푚 푦�푡+ℎ 푡 푚 훽̂1 푡+ℎ 푡 훽̂2 푡+ℎ 푡 � 휆푡푚 � 훽̂3 푡+ℎ 푡 � 휆푡푚 − 푒 � where h = 1, 6, 12, 24, 36, and 48 weeks; , | = + , j = 1, 2, 3, and and are obtained by regressing on an intercept and . , 훽̂푗 푡+ℎ 푡 푐푗̂ 훾�푗훽̂푗� 푐푗̂ 훾�푗 훽̂푗� 훽̂푗 푡−ℎ

The International Journal of Business and Finance Research ♦ VOLUME 6 ♦ NUMBER 4 ♦ 2012

ii. NSVAR

Based on a VAR(1) model for = , , , the predicted term structure of yield is

훽̂푡 �훽̂1푡 훽̂2푡 훽̂3푡�′ | ( ) = , | + , | + , | , (3) −휆푡푚 −휆푡푚 1−푒 1−푒 −휆푡푚 푡+ℎ 푡 1 푡+ℎ 푡 2 푡+ℎ 푡 푡 3 푡+ℎ 푡 푡 where푦� 푚 | =훽̂ + 훽,̂ and and� 휆 푚are �obtained훽̂ by �regressing휆 푚 − 푒 on an� intercept and .

훽̂푡+ℎ 푡 푐̂ 훤�훽̂푡 푐̂ 훤� 훽̂푡 훽̂푡−ℎ iii. NSARD

Following Equation (1), we use the univariate AR(1) model to estimate the three factors , , and by least squares, period by period, and then take , , and first differences as 훽̂1푡 훽̂2푡 3푡 1푡 2푡 3푡 훽̂ , = , , , j = 1, 2, 3. 훽 ̂ 훽 ̂ 훽 ̂

∆훽̂푗 푡 훽̂푗 푡 − 훽̂푗 푡−1 The forecasting coefficients , | are then

푗 푡+ℎ 푡 , | = + , , ∆훽̂ (4)

∆where훽̂푗 푡+ ℎh푡 = 1,푐 푗6,̂ 12,훾�푗 24,∙ ∆훽 ̂36,푗 푡 and 48 weeks.

iv. NSVARD Following Equation (1), we use the multivariate VAR(1) model to estimate the coefficients vector = , , , and then take the first difference as

훽̂푡 =�훽̂1푡 훽̂2푡 훽̂3.푡 �′ The∆훽̂푡 forecasting훽̂푡 − 훽̂푡− 1coefficients vector , | is then | = + . ∆훽̂푗 푡+ ℎ 푡 (5)

∆v.훽 ̂PCA푡+ℎ 푡R 푐̂ Γ�∆훽̂푡 We first decompose the yield covariance matrix of as by means of PCA in a time window of size , = , where the diagonal elements′ of are the eigenvalues, and the ∗ 푦푡 훧훧훧 ′ 1 푇 ∗ ′ columns of Z are 푡the=푇 −푚eigenvectors.+1 푡 푡 In order to obtain the first three principal components, , 푚 , 훧훧and훧 , 푚 we∑ take the largest푦 푦 three eigenvalues and their associated� eigenvectors. This can be written as , = , j = 1, 2, 3. In the next step, we take the AR(1) model to produce h-step-ahead 1푡 2푡 3푡 푝forecastŝ 푝̂ for 푝thê principal′ components 푝̂푗 푡 푧푗 푦푡

, | = + , , j=1, 2, 3. (6)

푝Finally,̂푗 푡+ℎ 푡 we푐 푗usê 훾the�푗푝 ̂푗PCA푡 model to produce h-step-ahead forecasts of the yields

| ( ) = ( ) , | + ( ) , | + ( ) , | , (7) where푦�푡+ℎ 푡 푚( ) is푧 1the푚 element푝1̂ 푡+ℎ 푡 for 푧the2 푚eigenvector푝̂2 푡+ℎ 푡 z푧 in3 maturity푚 푝̂3 푡+ℎ 푡 .

푧 푚 푚

MM. Kuo et al | IJBFR ♦ Vol. 6 ♦ No. 4 ♦ 2012

vi. PCAVAR

Following PCAR, the first three principal components are vector = ( , , ) . This can be shown as = . In the next step, we take the VAR(1) model to produce h-step-ahead forecasts for the principal components 푝̂푡 푝1̂ 푡 푝̂2푡 푝̂3푡 ′ 푝̂푡 푧′푦푡 | = + . (8)

푝Finally,̂푡+ℎ 푡 we푐̂ use훤� 푝thê푡 PCA model to produce h-step-ahead forecasts of the yields

| ( ) = ( ) , | + ( ) , | + ( ) , | , (9) where푦�푡+ℎ 푡 푚( ) is푧 1the푚 element푝1̂ 푡+ℎ 푡 for 푧the2 푚eigenvector푝̂2 푡+ℎ 푡 z푧 in3 maturity푚 푝̂3 푡+ℎ 푡 .

푧 푚 푚 vii. PCARD

Following PCAR, we obtain , , and , from , = , j = 1, 2, 3. In the next step, we take the first difference of the three principal components ′ 푝̂1푡 푝̂2푡 푝̂3푡 푝̂푗 푡 푧푗 푦푡 , = , , , j = 1, 2, 3.

푗 푡 푗 푡 푗 푡−1 ∆We푝̂ then 푝takê − the푝̂ AR(1) model to produce h-step-ahead forecasts for the principal components

, | = + , , j = 1, 2, 3. (10)

푗 푡+ℎ 푡 푗 푗 푗 푡 Finally,Δ푝̂ we use푐̂ the훾� ΔPCA푝̂ model to produce h-step-ahead forecasts of the yields. viii. PCVARD

Following PCVAR, we obtain = ( , , ) from = . In the next step, we take the first difference of the three principal components 푝̂푡 푝1̂ 푡 푝̂2푡 푝̂3푡 ′ 푝̂푡 푧′푦푡 = .

∆We푝̂푡 then푝 ̂푡take− 푝 ̂푡the−1 AR(1) model to produce h-step-ahead forecasts for the principal components

| = + . (11) 푡+ℎ 푡 � 푡 Finally,Δ푝̂ we푐 usê ΓtheΔ푝 ̂PCA model to produce h-step-ahead forecasts of the yields.

ix. RW

The h-step-ahead forecast of the term structure of the swap rates is

| ( ) = ( ). (12)

푦�푡+ℎ 푡 푚 푦푡 푚 x. YDARL

We use a univariate AR(1) model to produce h-step-ahead forecasts of the yield level

The International Journal of Business and Finance Research ♦ VOLUME 6 ♦ NUMBER 4 ♦ 2012

( ) = ( ) + ( ). (13)

푦�푡+ℎ⁄푡 푚 푐̂ 푚 훾�푦푡 푚 xi. YDVARL We use a multivariate VAR(1) model to produce h-step-ahead forecasts of the yield level

= + , (14)

푦where�푡+ℎ⁄푡 =푐̂ [ 훤�(푦1푡), (2), (3), (4), (5), (7), (10)] .

RESULTS푦푡 AND푦푡 DI푦S푡CUSSION푦푡 푦푡 푦푡 푦푡 푦푡 ′

In this section, we assess the out-of-sample predictive accuracies of various models. We estimate and forecast recursively. A 360-week rolling window of data is used to calibrate the model; for example, the first estimation uses 360-week data from April 29, 2002 to March 16, 2009 to estimate the model parameters, then proceeds with the out-of-sample 1-, 6-, 12-, 24-, 36-, and 48-week-ahead predictions. Further, the second estimation uses a 360-week rolling window of data from May 6, 2002 to March 23, 2009 for the next out-of-sample prediction. Therefore, there are total of 77 estimations. We estimate all competitor models in the same way.

To evaluate out-of-sample forecasting performance, we use root-mean-squared error (RMSE),

/ | / , 1 2 Model 2 �∑�푦푡+ℎ − 푦�푡+ℎ 푡 � 푛� where is the realized yield, | is the prediction estimated by the models, and n is the total number of estimations, i.e. 77. Model 푦푡+ℎ 푦�푡+ℎ 푡 The forecast performance for the shape of the swap yield curve is decomposed as level, slope, and curvature. We also use RMSE to evaluate out-of-sample forecasting performances

/ , , | / , 1 2 Model 2 ∑ 훽푗 푡+ℎ − 훽̂푗 푡+ℎ 푡 푛 �where� , is the realized� � parameters of the j factor, extracted from the realized yield rates, | is the prediction estimated by the models, and n is the total number of estimations, i.e. 77. Model 푗 푡+ℎ ̂푡+ℎ 푡 훽 훽 Forecasting Results

In Tables 2 and 3, we compare h-week-ahead out-of-sample forecasting performance across 11 models for maturities of 1, 2, 3, 4, 5, 7, and 10 years. Table 2 presents short-term RMSEs for the 1- and 6-week- ahead forecasts, and Table 3 presents the long-term RMSEs for the 12-, 24-, 36-, and 48-week-ahead forecasts, respectively. The last column in each model shows that model’s average RMSE across the 1-, 2-, 3-, 4-, 5-, 7-, and 10-year maturities. The bold number in the last column represents the lowest RMSE, and is the best forecasting model among the relative models.

From Panel A of Table 2, PCVARD has the lowest average RMSE, 0.110 for 1-week-ahead forecast; NSARD, PCARD, and RW have the second lowest average RMSEs, 0.111. On the other hand, NSARD has the lowest average RMSE, 0.223, and PCVARD has the second lowest average RMSE, 0.226 for 6- week-ahead forecasts in Panel B. Although the average RMSE for NSARD in a 1-week-ahead forecast

MM. Kuo et al | IJBFR ♦ Vol. 6 ♦ No. 4 ♦ 2012 is not the lowest RMSE, it is not significantly different from the RMSE of PCVARD. Our results show that both PCVARD and NSARD provide the superior forecasting performance for short-term predictive horizon.

Table 2: Swap Yield out-of-sample Short-term Forecast Evaluation

Panel A: 1-week-ahead Forecast Models 1yr 2yr 3yr 4yr 5yr 7yr 10yr Avg 1.NSAR 0.083 0.140 0.127 0.134 0.137 0.138 0.146 0.129 2.NSVAR 0.068 0.117 0.111 0.121 0.129 0.131 0.141 0.117 3.NSARD 0.065 0.108 0.103 0.117 0.128 0.128 0.131 0.111 4.NSVARD 0.068 0.110 0.104 0.117 0.128 0.129 0.132 0.113 5.PCAR 0.070 0.099 0.107 0.122 0.127 0.130 0.134 0.113 6.PCAVAR 0.067 0.097 0.111 0.127 0.131 0.132 0.137 0.115 7.PCARD 0.070 0.100 0.105 0.117 0.125 0.131 0.132 0.111 8.PCVARD 0.067 0.096 0.104 0.117 0.125 0.131 0.132 0.110 9.RW 0.067 0.091 0.108 0.118 0.124 0.130 0.137 0.111 10.YDARL 0.068 0.093 0.110 0.120 0.128 0.131 0.136 0.112 11.YDVARL 0.070 0.096 0.111 0.125 0.131 0.133 0.139 0.115 Panel B: 6-week-ahead Forecast Models 1yr 2yr 3yr 4yr 5yr 7yr 10yr Avg 1.NSAR 0.335 0.426 0.425 0.422 0.407 0.386 0.380 0.397 2.NSVAR 0.152 0.226 0.261 0.288 0.296 0.306 0.325 0.265 3.NSARD 0.121 0.168 0.190 0.229 0.256 0.285 0.316 0.223 4.NSVARD 0.127 0.174 0.195 0.233 0.259 0.288 0.318 0.228 5.PCAR 0.186 0.192 0.234 0.269 0.274 0.273 0.289 0.245 6.PCAVAR 0.154 0.196 0.262 0.304 0.311 0.304 0.321 0.265 7.PCARD 0.135 0.160 0.195 0.235 0.257 0.288 0.319 0.227 8.PCVARD 0.130 0.157 0.195 0.235 0.258 0.289 0.320 0.226 9.RW 0.141 0.164 0.196 0.232 0.258 0.284 0.314 0.227 10.YDARL 0.154 0.190 0.230 0.263 0.318 0.308 0.312 0.254 11.YDVARL 0.159 0.226 0.284 0.319 0.323 0.316 0.336 0.280 This table shows the results of the out-of-sample short-term forecasting evaluation based on the RMSE. Panels A and B present the results of 1- and 6-week-ahead forecasts, respectively. The last column Avg denotes the average RMSEs for 1, 2, 3, 4, 5, 7, and 10 years. The bold numbers present the lowest average RMSEs among these models. The italicized numbers present the lowest RMSE in a specific maturity. The results show that both PCVARD and NSARD provide the superior forecasting performance for short-term predictive horizon.

In Table 3, we find that NSARD provides the best and most reliable out-of-sample forecasting power in 12-, 24-, 36-, and 48-week-ahead forecasting on average, and the lowest average RMSEs are 0.291, 0.403, 0.446, and 0.416, respectively. PCARD (RMSE: 0.291) has the same RMSE with NSARD in 12- week-ahead forecasts, and the suboptimal model is PCVARD (RMSE: 0.292). Both PCARD (RMSE: 0.404) and PCVARD (RMSE: 0.404) are the suboptimal model in 24-week-ahead forecasts. In addition, for 36- and 48-week-ahead forecasts, PCVARD has the second lowest average RMSEs, 0.448 and 0.418, respectively. In summary, PCVARD always play suboptimal forecasting model across maturities, whatever short-term or long-term forecasts.

Further, our forecasting results for the NSARD or NSVARD are more stable than any other NSAR or NSVAR in each h-week-ahead forecast, respectively. We also find that the results of PCA are similar to the NS model, which is especially significant for long-term forecasting. The reason for this phenomenon is derived from the nonstationary processes of the AR or VAR models, which cause the first difference models to outperform the non-difference models. Also, NSAR always has the highest RMSE whatever short-term or long-term forecasting across all maturities. But after taking the first- order difference, it is surprising to observe that NSARD is almost superior for all under five-year maturity forecasting of swap yields in Tables 2 and 3.

The International Journal of Business and Finance Research ♦ VOLUME 6 ♦ NUMBER 4 ♦ 2012

Table 3: Swap Yield out-of-sample Long-term Forecast Evaluation

Panel A: 12-week-ahead Forecast Models 1yr 2yr 3yr 4yr 5yr 7yr 10yr Avg 1.NSAR 0.566 0.687 0.706 0.696 0.664 0.602 0.558 0.640 2.NSVAR 0.203 0.302 0.386 0.434 0.448 0.454 0.470 0.385 3.NSARD 0.146 0.186 0.245 0.302 0.341 0.383 0.430 0.291 4.NSVARD 0.151 0.189 0.247 0.304 0.343 0.385 0.432 0.293 5.PCAR 0.269 0.272 0.351 0.397 0.399 0.373 0.380 0.349 6.PCAVAR 0.206 0.265 0.387 0.453 0.469 0.449 0.465 0.385 7.PCARD 0.159 0.163 0.246 0.309 0.341 0.385 0.433 0.291 8.PCVARD 0.156 0.163 0.248 0.311 0.343 0.386 0.434 0.292 9.RW 0.179 0.183 0.259 0.306 0.343 0.381 0.422 0.296 10.YDARL 0.214 0.263 0.357 0.402 0.516 0.469 0.449 0.381 11.YDVARL 0.209 0.322 0.435 0.493 0.503 0.480 0.495 0.420 Panel B: 24-week-ahead Forecast Models 1yr 2yr 3yr 4yr 5yr 7yr 10yr Avg 1.NSAR 0.878 1.017 1.024 0.982 0.920 0.802 0.715 0.906 2.NSVAR 0.296 0.461 0.583 0.644 0.663 0.662 0.672 0.569 3.NSARD 0.189 0.254 0.355 0.429 0.474 0.528 0.591 0.403 4.NSVARD 0.192 0.257 0.357 0.432 0.476 0.530 0.593 0.405 5.PCAR 0.407 0.474 0.567 0.607 0.597 0.540 0.537 0.533 6.PCAVAR 0.298 0.426 0.581 0.661 0.684 0.652 0.667 0.567 7.PCARD 0.200 0.229 0.356 0.441 0.480 0.524 0.598 0.404 8.PCVARD 0.197 0.228 0.357 0.442 0.481 0.525 0.599 0.404 9.RW 0.240 0.270 0.391 0.458 0.491 0.523 0.565 0.420 10.YDARL 0.315 0.448 0.620 0.693 0.873 0.736 0.657 0.620 11.YDVARL 0.293 0.473 0.630 0.703 0.720 0.682 0.694 0.599 Panel C: 36-week-ahead Forecast Models 1yr 2yr 3yr 4yr 5yr 7yr 10yr Avg 1.NSAR 1.094 1.226 1.210 1.140 1.049 0.891 0.764 1.053 2.NSVAR 0.363 0.573 0.703 0.760 0.769 0.743 0.724 0.662 3.NSARD 0.209 0.284 0.408 0.488 0.536 0.579 0.622 0.446 4.NSVARD 0.214 0.289 0.411 0.491 0.538 0.581 0.623 0.449 5.PCAR 0.511 0.626 0.717 0.741 0.713 0.616 0.572 0.643 6.PCAVAR 0.366 0.538 0.700 0.776 0.790 0.730 0.718 0.660 7.PCARD 0.220 0.263 0.411 0.501 0.548 0.573 0.630 0.449 8.PCVARD 0.216 0.260 0.410 0.500 0.548 0.573 0.630 0.448 9.RW 0.269 0.331 0.473 0.542 0.570 0.568 0.576 0.475 10.YDARL 0.385 0.590 0.806 0.886 1.089 0.881 0.732 0.767 11.YDVARL 0.371 0.569 0.732 0.801 0.809 0.742 0.725 0.678 Panel D: 48-week-ahead Forecast Models 1yr 2yr 3yr 4yr 5yr 7yr 10yr Avg 1.NSAR 1.248 1.416 1.410 1.329 1.221 1.031 0.865 1.217 2.NSVAR 0.392 0.670 0.823 0.879 0.879 0.838 0.801 0.755 3.NSARD 0.200 0.296 0.426 0.478 0.496 0.499 0.520 0.416 4.NSVARD 0.205 0.301 0.429 0.481 0.498 0.501 0.521 0.419 5.PCAR 0.588 0.757 0.862 0.875 0.828 0.696 0.617 0.746 6.PCAVAR 0.396 0.633 0.819 0.894 0.900 0.821 0.795 0.751 7.PCARD 0.208 0.270 0.427 0.495 0.516 0.493 0.525 0.419 8.PCVARD 0.204 0.267 0.426 0.495 0.516 0.493 0.526 0.418 9.RW 0.265 0.384 0.540 0.592 0.591 0.525 0.481 0.483 10.YDARL 0.427 0.723 0.978 1.056 1.278 1.027 0.834 0.903 11.YDVARL 0.406 0.659 0.841 0.909 0.909 0.822 0.789 0.762 This table shows the results of the out-of-sample long-term forecasting evaluation based on the RMSE. Panels A, B, C, and D present the results for 12-, 24-, 36-, and 48-week-ahead forecasts, respectively. The last column Avg presents the average RMSEs for 1, 2, 3, 4, 5, 7, and 10 years. The bold numbers present the lowest average RMSEs among these models. The italicized numbers present the lowest RMSE in a specific maturity. The results show that NSARD provides the best and most reliable out-of-sample forecasting power for long-term predictive horizon.

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Apparently, the NSARD provides more reliable for 1-5 years maturities forecasts and the first-order difference of the estimated parameters deeply improve the predictability of NS model. Although NSARD is not the lowest RMSE for over five-year maturities among swap yields, it is not the worst model. We also find that it is difficult to be sure which is the best model for the over five-year maturities in swap yield forecasting, but it almost certainly RW. These findings suggest that there is poor predictability in long-term rates.

In Table 4, we make comparisons with the overall forecasting horizon performance. The short-term average presents average RMSEs for the 1- and 6-week-ahead forecasts. The long-term average presents average RMSEs for the 12-, 24-, 36-, and 48-week-ahead forecasts. The second-to-last column presents the average RMSEs for short-term and long-term forecasts. We find that NSARD produces superior out-of-sample forecasts of swap yield in the long-term horizon. In the short term, the forecasting performance of NSARD has no significant difference with other models like, PCVARD and RW. Over all, we find the most efficient model is NSARD, and the worst model is NSAR. The results confirm that the first-order difference of the estimated parameters results in more stable forecasting errors.

Table 4: Swap Yield out-of-sample Forecast Evaluation

1-week- 6-week- 12-week- 24-week- 36-week- 48-week- Short- Long- Models Avg Rank ahead ahead ahead ahead ahead ahead term term 1.NSAR 0.129 0.397 0.640 0.906 1.053 1.217 0.263 0.954 0.724 11 2.NSVAR 0.117 0.265 0.385 0.569 0.662 0.755 0.191 0.593 0.459 8 3.NSARD 0.111 0.223 0.291 0.403 0.446 0.416 0.167 0.389 0.315 1 4.NSVARD 0.113 0.228 0.293 0.405 0.449 0.419 0.170 0.392 0.318 4 5.PCAR 0.113 0.245 0.349 0.533 0.643 0.746 0.179 0.568 0.438 6 6.PCAVAR 0.115 0.265 0.385 0.567 0.660 0.751 0.190 0.591 0.457 7 7.PCARD 0.111 0.227 0.291 0.404 0.449 0.419 0.169 0.391 0.317 3 8.PCVARD 0.110 0.226 0.292 0.404 0.448 0.418 0.168 0.391 0.316 2 9.RW 0.111 0.227 0.296 0.420 0.475 0.483 0.169 0.419 0.335 5 10.YDARL 0.112 0.254 0.381 0.620 0.767 0.903 0.183 0.668 0.506 10 11.YDVARL 0.115 0.280 0.420 0.599 0.678 0.762 0.198 0.615 0.476 9 This table shows the comparisons with the overall forecasting horizon performance. The short-term average presents the average RMSEs of the 1-, and 6-week-ahead forecasts. The long-term average presents the average RMSEs of the 12-, 24-, 36-, and 48-week-ahead forecasts. The second-to-last column Avg presents the average RMSEs for 1-, 6-, 12-, 24-, 36-, and 48-week-ahead forecasts. The bold numbers present the lowest average RMSEs among these models. The italicized numbers present the lowest RMSE in a specific maturity. The results show that NSARD dominates the other models no matter for short-term or long-term forecasts.

According to the macroeconomics literature, unrestricted VAR tend to produce poor predictions. The accuracy and precision of the VAR-based forecast depend on the time heterogeneity properties of the series. Unless the VAR model is stable, it will generate poor forecasting over long horizons. We find that NSVAR is better than NSAR; however, PCAVAR is worse than PCAR. Apparently, AR models do not absolutely outperform VAR in this paper, but after taking the first-order difference, we find that the forecasting performances of NSARD versus NSVARD or PCARD versus PCVARD are almost equal.

Comparison of Forecasting Performance of the Shape of the Swap Yield Curve

In this subsection, we predict the dynamics of changes in level, slope, and curvature coefficients. Table 5 reports the evidence on out-of-sample predictability in the shape of the swap yield curve. We first run in- sample AR(1) regressions of changes in the beta parameters to obtain coefficients for level, slope, and curvature, then compare these with the realized coefficients to calculate RMSEs. If the model is able to reflect the future basic shape of the swap yield curve, then it should be able to reveal a better maturity- specific pattern.

The International Journal of Business and Finance Research ♦ VOLUME 6 ♦ NUMBER 4 ♦ 2012

In Table 5, we find that: the NSARD in predicting the level factor has the lowest short-term forecast average RMSE at 0.115, the long-term average RMSE is 0.209, and the total average RMSE is 0.177. For the slope factor, PCAR is best for the short-term (RMSE: 0.222) and long-term (RMSE: 0.467) forecast horizons. Finally, YDARL is superior for short-term (RMSE: 0.162) and long-term (RMSE: 0.297) forecast horizons in predicting the curvature factor. According to Litterman and Scheinkman (1991), the level factor accounts for an average of 89.5 percent of the observed variation in yield changes across maturities. If our data are analogous to those of Litterman and Scheinkman, this may be why the NSARD becomes the best model for forecasting level factor and swap yield curve. Meanwhile, it is interesting to find that the ranks for level factor in Table 5 are similar to that for swap yield in Table 4, especially for ranks from 1 to 5. This also can explain why level factor accounts for a large part of the variation in yield changes.

CONCLUSION

The goal of this study is to investigate the most efficient model for forecasting the HIBOR swap yield curve and survey the predictability in the shape of the swap yield curve for different models. In emerging markets, government bond markets lack liquidity, so the swap rates become the important proxies for the spot rates for these financial participants. Due to the lack of attention paid to forecasting the swap yield and term structures of emerging markets, we employ various models to forecasting HIBOR swap yield and term structures. Accurate estimate of the current term structure of interest rates is an important issue in finance. This requirement can be evaluated using the out-of-sample fit.

In this study, we propose an explicitly out-of-sample forecasting perspective using HIBOR swap yields, and investigate various prediction-horizon forecasts to compare a total of eleven models. At the same time, we try to exploit predictability in the shape of the HIBOR swap term structure. HIBOR swap yield are weekly data, and taken form DATASTREAM over the period April 29 2002 to July 25 2011.

We find that NSARD outperforms other competitors on out-of-sample forecasting accuracy, especially on maturities under five years, for almost all prediction-horizons. Our robust empirical evidence supports the remarkable out-of-sample forecasting performance of the NS dynamic model. Further, we find that a methodology using lagged explanatory variables to predict changes in yield will improve out-of-sample predictability for both the NS and PCA models.

Similarly, we also find that when we use lagged explanatory variables to predict changes in the level, slope, and curvature coefficients, rather than past values of these parameters as in Diebold and Li (2006), this enhances the out-of-sample predictability of these models. The evidence for out-of-sample predictability is analogous to Fabozzi et al. (2005).

On the other hand, we try to compare eleven models to predict the shape of the swap term structure. The empirical evidence indicates that NSARD provides the best predictability in the level factor of the HIBOR swap yield curve, contrary to the results of Fabozzi et al. (2005), by using data from US government bond yield and swap rates. Finally, we conclude that the preferred model should depend on the nature of the data and the forecast horizons. Thus, the main contribution of this paper is to bridge the gap in the literature by forecasting the term structure of swap rates and the predictability in the shape of the swap yield curve and find the most efficient model for predicting the HIBOR swap yield and term structures.

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Table 5: Shape of HIBOR SWAP Yield Curve Forecast Evaluation

1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. Models NS- NS- NS- NS- PC- PCA- PC- PC- YD- YD- RW AR VAR ARD VARD AR VAR ARD VARD ARL VARL Level

1wk 0.110 0.092 0.084 0.086 0.089 0.088 0.086 0.084 0.085 0.087 0.090 6wk 0.348 0.185 0.146 0.152 0.177 0.178 0.151 0.149 0.153 0.178 0.204 12wk 0.568 0.263 0.177 0.180 0.248 0.255 0.177 0.177 0.187 0.262 0.301 24wk 0.821 0.415 0.231 0.235 0.416 0.406 0.234 0.233 0.253 0.420 0.437 36wk 0.983 0.494 0.231 0.236 0.521 0.484 0.237 0.235 0.271 0.519 0.501 48wk 1.150 0.576 0.195 0.200 0.627 0.565 0.200 0.198 0.281 0.631 0.572 Short- term 0.229 0.139 0.115 0.119 0.133 0.133 0.119 0.117 0.119 0.133 0.147 Long- term 0.881 0.437 0.209 0.213 0.453 0.428 0.212 0.211 0.248 0.458 0.453 Avg 0.663 0.338 0.177 0.182 0.346 0.329 0.181 0.179 0.205 0.350 0.351 Rank 11 7 1 4 8 6 3 2 5 9 10 Slope

1wk 0.118 0.130 0.122 0.122 0.117 0.121 0.119 0.119 0.125 0.122 0.123 6wk 0.333 0.360 0.356 0.357 0.326 0.352 0.362 0.363 0.362 0.329 0.335 12wk 0.458 0.509 0.502 0.502 0.433 0.498 0.509 0.509 0.501 0.449 0.465 24wk 0.666 0.648 0.705 0.704 0.519 0.638 0.715 0.715 0.679 0.611 0.627 36wk 0.755 0.671 0.771 0.770 0.513 0.659 0.781 0.781 0.722 0.644 0.651 48wk 0.680 0.669 0.679 0.679 0.402 0.656 0.685 0.685 0.605 0.615 0.658 Short- term 0.226 0.245 0.239 0.240 0.222 0.237 0.241 0.241 0.244 0.226 0.229 Long- term 0.640 0.624 0.664 0.664 0.467 0.613 0.673 0.673 0.627 0.580 0.600 Avg 0.502 0.498 0.523 0.522 0.385 0.487 0.529 0.529 0.499 0.462 0.477 Rank 7 5 9 8 1 4 10 11 6 2 3 Curvature

1wk 0.170 0.160 0.168 0.169 0.115 0.118 0.135 0.135 0.108 0.107 0.111 6wk 0.328 0.274 0.297 0.298 0.219 0.224 0.259 0.259 0.239 0.217 0.234 12wk 0.445 0.312 0.357 0.357 0.264 0.258 0.308 0.307 0.281 0.247 0.280 24wk 0.663 0.390 0.495 0.494 0.389 0.330 0.428 0.427 0.396 0.311 0.367 36wk 0.766 0.391 0.565 0.565 0.452 0.327 0.487 0.487 0.435 0.314 0.358 48wk 0.820 0.388 0.562 0.563 0.485 0.325 0.473 0.473 0.415 0.314 0.366 Short- term 0.249 0.217 0.233 0.234 0.167 0.171 0.197 0.197 0.174 0.162 0.173 Long- term 0.674 0.370 0.495 0.495 0.398 0.310 0.424 0.424 0.382 0.297 0.343 Avg 0.532 0.319 0.407 0.408 0.321 0.264 0.348 0.348 0.312 0.252 0.286 Rank 11 5 9 10 6 2 8 7 4 1 3 This table shows RMSEs of the forecasting the shape of the HIBOR swap yield curve for all h-week-ahead in all modes. The short-term average presents the average RMSEs of the 1- and 6- week-ahead forecasts. The long-term average presents the average RMSEs of the 12-, 24-, 36-, and 48-week-ahead forecasts. The second to last column Avg presents the average RMSEs for 1-, 6-, 12-, 24-, 36-, and 48-week- ahead forecasts. The bold numbers present the lowest average RMSEs among those models. The last raw shows the rank for the average RMSEs. The rank 1 presents the lowest average RMSE, that is, the best forecasting performance.

Limitations

In spite of dynamic NS model has good empirical performance, however, the NS model does not enforce arbitrage-free consistency over time (see Diebold and Li, 2006). It is therefore interesting to extend this research to introduce a closely related generalized Nelson–Siegel model on which the arbitrage-free condition can be imposed. Finally, another possible future research is to extend this study to across different emerging markets, because the preferred model could depend on the nature of the data and the forecast horizons.

The International Journal of Business and Finance Research ♦ VOLUME 6 ♦ NUMBER 4 ♦ 2012

REFERENCES

Blaskowitz, O. and H. Herwartz (2009) “Adaptive Forecasting of the EURIBOR Swap Term Structure,” Journal of Forecasting, 28, p. 575-594.

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Dai, Q. and K. Singleton (2000) “Specification Analysis of Affine Term Structure Models,” Journal of Finance, 55, p. 1943-1978. de Jong, F. (2000) “Time Series and Cross Section Information in Affine Term Structure Models,” Journal of Business and Economic Statistics, 18, p. 300-314.

Diebold, F.X. and C. Li (2006) “Forecasting the Term Structure of Government Bond Yields,” Journal of Econometrics, 130, p. 337-364.

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Litterman, R. and J. Scheinkman (1991) “Common Factors Affecting Bond Returns,” Journal of Fixed Income, 1, p. 54-61.

Nelson, C.R. and A.F. Siegel (1987) “Parsimonious Modeling of Yield Curve,” Journal of Business, 60, p. 473-489.

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Yu, W.C. and E. Zivot (2011) “Forecasting the Term Structures of Treasury and Corporate Yields Using Dynamic Nelson-Siegel Models,” International Journal of Forecasting, 27, p. 579-591.

ACKNOWLEDGMENT

We are grateful to the Editor Mercedes Jalbert and two anonymous referees for helpful comments and suggestions.

BIOGRAPHY

Ms. Mei-Mei Kuo, corresponding author, is a Ph.D. candidate in the Department of Business Administration, National Taiwan University of Science and Technology. Her e-mail address is: [email protected]

Dr. Shih-Wen Tai is an assistance professor of Business Administration at Lunghwa University of Science and Technology, Taiwan. His e-mail address is: [email protected]

Dr. Bing-Huei Lin is a professor of Finance at National Chung Hsing University, Taiwan. His e-mail address is: [email protected]

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COST EFFICIENCY OF FRENCH COMMERCIAL BANKS: DOMESTIC VERSUS FOREIGN BANKS Raoudha Béjaoui Rouissi, LEFA – IHEC Carthage Houssam Bouzgarrou, CREM University of Rennes 1

ABSTARCT

The present paper investigates efficiency levels of commercial domestic versus foreign banks in France between 2000 and 2007. Using Stochastic Frontier Analysis, we also shed light on the determinants of cost efficiency of 62 domestic and 40 foreign banks. Our results indicate that foreign banks exhibit higher cost efficiency than domestic banks. This finding contradicts previous empirical literature, concluding an advantage of cost efficiency for domestic banks in developed countries such as France (Berger et al. 2000). This suggests the decision to practice retail banking activities by domestic banks implies high fixed costs.

JEL: G21; C23; D24

KEYWORDS: efficiency, domestic banks, foreign banks

INTRODUCTION

he transposition of the 1988s European Directive, on the free movement of capital into national law, eliminated lending restrictions and currency controls and removed many administrative barriers that Thad compartmentalized the credit institution business in European countries. This had a large impact on traditional banking intermediation business. It gave rise to disintermediation in lending and an alignment of bank lending rates and terms to those of the market. Furthermore, gradual liberalization of financial markets that started in the mid-1980s in France increased competition from non-banks, such as mutual funds and insurance companies. Most notably, it stimulated competition from markets. An important resulting change in the French banking industry was a substantial decrease in the population of banks. At the same time, the number of foreign owned banks in France rose between 1984 and 2001.

Greater openness in the French banking market includes increased openness to foreign-owned banks, with the intention of improving competitiveness and efficiency of the financial system. However, Weill (2006b) notes that reduced performance of French banks allows entry of foreign banks in France, since foreign banks are able to significantly affect the performance of domestic banks. Poor results of French banks signify the possibility of easier entry of foreign banks on the French market.

This study compares the efficiency of foreign-owned banks operating in France with French domestic banks of the French banking system. The objective is to determine if foreign banks were more or less cost efficient than domestic banks during our estimation period of 2000-2007. Thus, the purpose is to investigate the efficiency levels of commercial domestic versus foreign banks in France by comparing basic accounting ratios and the stochastic cost frontier analysis (SFA). We analyze cost efficiency by using an unbalanced sample, including 62 domestic and 40 foreign banks over the period 2000-2007.

The outline of this paper is as follows. Section 2 provides an overview of previous studies that have considered; (i) the efficiency of the French banking system and (ii) the efficiency of foreign banks. Section 3 discusses the data and methodology employed, while the fourth section discusses the results. The final section provides conclusions.

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LITERATURE REVIEW

There are two relevant streams of literature to this study, (i) those dealing with bank efficiency in France, and (ii) those comparing foreign bank efficiency with domestic bank efficiency. The literature on efficiency was applied to banks only during the 90s. A small number of studies focused on the efficiency of French banks. We can however distinguish two categories: studies that focus entirely on French banks; and studies consisting of international comparisons of bank efficiency.

Studies on French banking efficiency were performed by Dietsch (1996), Dietsch and Weill (1999) and Weill (2006b). Dietsch (1996) uses a parametric method, the Distribution Free Approach to estimate the cost efficiency of 375 commercial and savings banks, over the period 1988-1992. The results show the existence of an average cost efficiency of 56.1% and 70.7%, with a truncation of 1% and 5%, respectively. The analysis of the relationship between the cost efficiency and the risk-taking supports the assumption that less efficient banks take excessive risks. Dietsch and Weill (1999) use a nonparametric method, Data Envelopment Analysis, to measure the technical efficiency of 93 French deposit banks in 1994. The average scores vary between 78% and 91%, depending on the retained productive combination. Personnel and interest expenses relative to total borrowed funds and other non-financial expenses represent the inputs. Credits, demand deposits, savings and other remunerated assets represent the outputs. The results show the lack of a clear relationship between the size and the existence of a negative relationship with the risk-taking.

Weill (2006b) analyzes the evolution of cost efficiency of 93 French banks, over the period 1992-2000. The author uses two parametric approaches to calculate the cost efficiency scores: the Stochastic Frontier Approach and a system of equations composed of a Fourier-flexible cost function and its associated input cost share equations derived using the Sheppard's lemma. The results show an increase in cost efficiency between 1992 and 2000, the average scores going from 77.20% to 83.98%. According to the Rosse-Panzar competition test, the increase in efficiency is not related to an increase in competition. Weill (2006b) also tests for convergence in French banks' efficiency, finding convergence over the period 1992-2000. This finding implies a catching-up process of the least efficient banks over the last decade.

Besides studies entirely orientated toward French banks, an important number of international bank efficiency tests exist. Two categories of international comparisons can be distinguished: First, those estimating a national frontier for each country (Berger et al. 2000, and Weill, 2004). Second those estimating common frontiers to several countries as a whole (Allen & Rai, 1996, Pastor, Pérez & Quesada, 1997, Chaffai & Dietsch, 1999, Dietsch & Lozano-Vivas, 2000, Altunbas et al. 2001, Chaffai, Dietsch & Lozano-Vivas, 2001, Lozano-Vivas, Pastor & Hasan, 2001 and Vander Vennet, 2002). Berger et al. (2000), from the first category noted above, use the Stochastic Frontier Approach (SFA) to estimate the cost and production frontiers for five countries (France, Germany, Spain, the United Kingdom and the United States). The results show an average cost efficiency of 70.9% in France, 79.3% in Germany, 91.5% in Spain, 79.1% in the UK and 77.4% in the US. Results of the large majority of studies consist of an average efficiency score between 70% and 80%.

Research on domestic versus foreign bank efficiency has expanded in recent years. The literature on foreign banking suggests that foreign banks may be less subject to domestic credit allocation rules than domestic banks and domestic banks may have informational advantages relative to foreign banks (Claessens & al, 2001).

Berger and Humphrey (1997) survey 130 efficiency studies of financial institutions, of which a few address the impact of foreign ownership. They suggest that a general conclusion regarding the efficiency effect of foreign ownership cannot be drawn based on the available empirical literature. The relative efficiency of foreign vs. Domestic ownership appears to depend on host and home country conditions. Berger et al. (2000), for instance, provide empirical evidence that foreign banks, in transition and developing markets,

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show higher efficiency compared to their domestically owned counterparts. However, foreign banks in developed countries exhibit lower efficiency than domestic banks. The authors perform an analysis of cross-border banking efficiency in France, Germany, Spain, the UK, and the US during the 1990s. On average, they find that domestic banks in these countries have both higher cost efficiency and higher profit efficiency than foreign banks operating in the country. Thus, the relative efficiency of foreign vs. domestic ownership appears to depend on host and home country conditions.

Berger et al. (2000) differentiate between “home field advantages” and “global advantages”. The “global advantages” hypothesis states that foreign banks benefit from competitive advantages relative to their domestically owned peers. Because of a stiff home market competition, foreign-owned banks use more advanced technologies. Foreign banks might also become more competitive when compared to domestic banks due to an active market for corporate control in the home country, and because they have access to an educated labor force that is able to adapt new technologies. Similarly, Havrylchyk (2006) suggests that foreign banks might profit from better risk management, and reliance on modern information technologies. The “home field advantages” hypothesis predicts foreign banks suffer from disadvantages when compared to domestic banks. Foreign-controlled banks are assumed to perform less well than domestically controlled banks due to higher costs of providing the same financial services or due to lower revenues.

Recent literature surveys confirm this conclusion. On one hand, it shows that foreign banks in transition and developing markets are more efficient than domestically owned banks. On the other hand, it shows that foreign banks in developed countries exhibit lower efficiency relative to domestic banks (Isik & Hassan, 2002, Jemric & Vujcic, 2002, Miller & Parkhe, 2002, Nikiel & Opiela, 2002, Hasan & Marton, 2003, Weill, 2003, Green et al. 2004, Sturm & Williams, 2004, Bonin et al. 2005, Fries & Taci, 2005, Havrylchyk, 2006, Zajc, 2006, Weill, 2006a, Lensink et al. 2008 and Berger et al. 2009).

METHODOLOGY

Our sample is an unbalanced panel that includes financial data of 102 French commercial banks, including 62 domestic and 40 foreign banks during the period 2000-2007. Income and Balance Sheet data was obtained from IBCA’s BANKSCOPE data set. Domestic banks are defined as banks whose state and/or private domestic ownership is 100% of total ownership. Majority foreign banks are defined as banks whose foreign ownership is 100% of total ownership.

Cost efficiency measure how well a bank is predicted to perform relative to a best-practice bank producing the same outputs under the same environmental conditions. We start by assuming that underlying technologies of domestic and foreign banking service productions in France are similar. This assumption allows us to correctly define a common frontier. Pooling all banks would implicitly assume efficiency differences across banks are attributed, entirely, to managerial decisions within banks regarding the scale and mix of inputs. In other words, a common frontier is based on the belief that efficiency differences across banks are primarily attributable to managerial decisions within banks. Banking technology can be defined as the set of specific methods that banks use to combine financial and physical inputs to generate a certain amount of banking services, such as liquidity and payment services, portfolio services and loan services. These methods are diversification, risk pooling, financial information collection and evaluation, risk management, and so on. There is a presumption that the technology used by domestic and foreign banks in France should be the same. However, bank-specific variables are taken into account because we believe these variables are major factors in explaining the differences in the banking cost. Thus, we use the common frontier approach to compare domestic and foreign banks of the French banking industry. We believe that efficiency differences between banks are determined by bank specific rather than technological differences (Dietsch & Lozano-Vivas, 2000).

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To measure cost efficiency of French banks we employ the stochastic frontier approach (SFA). The SFA specifies a particular form for the cost function, usually a translog form, and allows for random error. It assumes these errors consist of inefficiencies, following an asymmetric distribution, usually a truncated or half-normal distribution, and random errors following a symmetric distribution, usually the standard normal distribution. The reason for this composite error term structure is that, by definition, inefficiencies cannot be negative. Both the inefficiencies and random errors are assumed to be orthogonal, to input prices, outputs and country-level or bank-specific variables specified in the estimating equation. We estimate efficiency levels by specifying the commonly-used translog functional form for the cost function, presented as follows:

Ln TC = α0 + ∑ βi Ln yi + ∑ β j Ln w j +1/ 2∑∑λik Ln yi Ln yk +1/ 2∑∑ξ jh Ln w j Ln w h i j i k j h

+ ∑∑ωij Ln yi Ln w j + ui + vi i j

Where TC is the bank’s total costs; yi; i=1,2 are outputs; and wj , j=1,2,3, are inputs prices. The homogeneity restrictions are imposed by normalizing total costs and input prices by one of the input prices λ = λ ∑β j = 1 , ∑ξh = 0 , ∑ωk = 0 and the symmetry restriction is ik ki j h k Nevertheless, our approach estimates not only the cost efficiency scores of French banks but also identifies determinants that affect these scores. Therefore, we adopt the Battese and Coelli (1995) approach, where

ui , the technical inefficiency effect, is assumed to be a function of a set of bank specific variables that can be specified by the equation ui = ziδ + wi , where the random variable, wi , is defined by the truncation of 2 the normal distribution N(0,σ ) , such that the point of truncation is − ziδ i.e. w i ≥ −z i δ . These 2 assumptions are consistent with ui being a non-negative truncation of the N(ziδ ,σ )

This paper used the intermediation approach to define outputs and inputs of a banking firm, which views the bank as employing labor, physical capital, and borrowed funds to produce earning assets, as first proposed by Sealey and Lindley (1977). This approach is commonly used in the conventional bank cost function literature. Two outputs are included in the model: Y1 = loans and Y2 = earning assets including negotiable certificates of deposit, all other negotiable debt instruments and equity investments. The inputs include labor, physical capital, and deposits. The first input price is the price of labor (w1) defined as the ratio of personnel expenses scaled by total assets. Although scaling over total employees, instead of total assets, gives a better proxy of the price of labor, the latter is chosen since for many observations the former is not available. The price of capital (w2) is constructed as depreciation and other non interest expenses to fixed assets. The third input (w3) is the price of funds (financial factor). It is defined as the ratio of a bank’s interest expenses scaled by the sum of deposits and other interest bearing funding. Total costs (TC) are the sum of staff expenses, depreciation and other non-interest expenses and interest expenses. We scale total costs, the price of labor and the price of capital by price of funds to guarantee linear homogeneity of the cost function.

To study the determinants of bank efficiency, the second analysis is to explore the characteristics of inefficient banks. Varieties of financial ratios are applied for this evaluation to provide indications for a bank’s technical efficiency. First, the return on assets (ROA), measured by profits before taxes to total assets. The second ratio is equity to total assets (EQTA). The third ratio is bank’s loans divided by customers and short term funding (LCSTF). The fourth ratio is bank size, measured by the logarithm of total assets. The fifth ratio is loan-loss provision to total loans (LLPCR). The sixth ratio is off-balance-sheet activities to total assets and off-balance-sheet activities (OBS). The last variable is foreign ownership, it is a

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dummy variable equals to 1 for foreign banks and equals to Zero for domestic banks. The cost function model is:

2  TC   w   w  1   w    = β + β  1  + β  2  + β + β + β   1  + Ln  0 1Ln  2 Ln  3 LnY1it 4 LnY2it 5  Ln  it  w3 it  w3 it  w3 it 2   w3  2     1  w2   w1   w2  1 2 1 2 β  Ln  it + β  Ln Ln  + β (LnY ) it + β (LnY ) it + β (LnY LnY ) + 2 6   w  7   w   w  2 8 1 2 9 2 10 1 2 it   3    3   3 it   w     w     w     w   β  Ln 1 LnY  + β  Ln 1 LnY  + β  Ln 2 LnY  + β  Ln 2 LnY  + 11   w  1  12   w  2  13   w  1  14   w  2    3  it   3  it   3  it   3  it

ν it + uit (1)

Where i, t index the bank and year, respectively, and cost efficiency determinants are defined as:

uit = δ1ROAit + δ 2 EQTAit + δ 3 LCSTFit + δ 4 LnTAit + δ 5 LLPCRit + δ 6OBSit + δ 7 foreignit + wit (2)

Models (1) and (2) are simultaneously estimated using the maximum likelihood parameter estimation. The computer program, FRONTIER Version 4.1 developed by (Battese & Coelli, 1995) was used to obtain the maximum likelihood estimates of parameters in estimating the technical efficiency. The program can accommodate cross sectional and panel data; cost and production function; half-normal and truncated normal distributions; time-varying and invariant efficiency; and functional forms which have a dependent variable in logged or original units.

EMPIRICAL RESULTS

Descriptive Statistics

Table 1 presents bank characteristics and financial performance measures. The average values of total loans and earning assets varies greatly between the two groups, from 2,484 and 2,739 million € for domestic banks to 797 and 1,008 million € for foreign banks. We show similar findings with the average values of total assets, total costs and operating profit. Regarding equity, domestic banks have a lower equity-to-asset ratio (9.24%) than foreign banks (10.65%). Interestingly, the provision-to-loan ratio of foreign banks is relatively higher than the domestic banks ratio, 0.9% and 0.5%, respectively. This result suggests that foreign banks operate with high non-performing loan level on the one hand, and prudence and ability to set aside such reserves on the other hand. This is consolidated by the average value of off balance sheet activities ratio, where foreign banks have a high ratio (27%) than domestic banks (24%).

Bank Efficiency by Ownership Type

To compare the two samples of domestic and foreign banks, we use the separate frontier approach. Table 2 presents the results of separate frontiers of cost efficiency for domestic and foreign banks. Note that a negative sign indicates a negative impact of the variable on the bank inefficiency and therefore a positive effect on cost efficiency.

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Table 1: Descriptive Statistics of Variables Used in the Cost Efficiency Estimation

Domestic Banks Foreign Banks

Mean SD Min Max Mean SD Min Max Output quantities (in

million €) Total loans (Y1) 2,484.4 4,045.3 3.900 30,789 796.83 1,305.6 7.500 11,497 Earning assets (Y2 ) 2,739.1 5,744.8 1.700 52,335 1,008 1,724.8 5.300 11,644

Input prices Price of labor (w1) 0.017 0.019 0.000 0.164 0.022 0.022 0.000 0.143 Price of capital (w2) 0.274 0.283 0.006 3.000 0.252 0.222 0.015 1.000 Price of funds (w3) 0.177 1.026 0.004 17.925 0.051 0.095 0.001 1.121

Cost (in million €) Total costs (TC) 311.98 506.85 1.800 3,460.3 91.411 138.53 2.900 1,136.6

Bank efficiency

determinants ROA 0.016 0.032 -0.157 0.343 0.013 0.027 -0.155 0.384 EQTA 0.092 0.121 0.002 0.883 0.106 0.085 0.002 0.556 LCSTF 1.288 2.815 0.001 37.777 1.108 4.274 0.023 50.382 TA (in million €) 5,566.2 8,224.2 105.10 60,789 2,015.5 2,778.5 48.70 16,325 LLPCR 0.005 0.028 -0.235 0.353 0.009 0.043 -0.133 0.470 OBS 0.240 0.180 0.000 0.856 0.271 0.181 0.000 0.869

Observations 485 320 The output variables considered are: total loans and earning assets. The input prices variables are: price of labor, defined as the ratio of personnel expenses scaled by total assets, price of capital, measured as depreciation and other non-interest expenses to fixed assets, and price of funds, defined as the ratio of a bank’s interest expenses scaled by the sum of deposits and other interest bearing funding. Total costs include both financial and operating costs and are defined as the sum of staff expenses, interest expenses and depreciation and other non interest expenses. Bank-specific factors are : (1) return on assets (ROA) measured by profits before taxes to total assets ; (2) the ratio of equity to assets (EQTA); (3) the ratio of bank’s loans divided by customers and short term funding (LCSTF) ; (4) bank size measured by the log of total assets ; (5) ratio of loan-loss provision to total loans (LLPCR); (6) ratio of off-balance-sheet activities to total assets and off-balance-sheet activities (OBS); and (7) a foreign ownership variable is a dummy variable equals to 1 for foreign banks and equals to 0 for domestic banks. σ 2 The variance parameter (γ = 2 2 ) estimates equal 81.4% and 79.4% respectively for domestic and σ + σ v foreign banks and are significant at the 1% level. The value of the likelihood-ratio test of the null hypotheses LR, that the inefficiency effects are absent or that they have simpler distributions equals 145.81 (281.14) for domestic (foreign) banks efficiency and are accepted at the 1% level of significance. This indicates that the joint effect of these explanatory variables on the inefficiencies is significant.

The ROA coefficient is positive and significant at the 1% level for domestic banks, indicating it has a negative effect on the cost efficiency. This variable measures the quality of management, shows that domestic banks operate with higher costs, so they are less cost efficient. Nevertheless, the ROA coefficient is negative and significant at the 10% level for foreign banks, which indicates that it has a positive effect on the cost efficiency. The possible outcome is that foreign banks are able to overcome any cross-border disadvantages and operate more efficiently than domestic banks. The higher efficiency occurs as a result of spreading superior managerial skills or best-practice policies and procedures, of obtaining diversification of risks that allows for higher-risk, higher expected-return investments, or of providing services of superior quality or variety that raises revenues (Berger et al. 2000).

Equity to assets (EQTA) is negative and significant at the 1% level related to cost efficiency for domestic banks (i.e. positive effect on inefficiency). This finding provides the argument that domestic banks invest more in risky assets. This result is confirmed by the coefficient sign’s of the OBS variable, which has a positive and significant effect on the cost efficiency.

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Table 2: Determinants of Bank Inefficiency by Ownership Type

Domestic banks Foreign banks ROA 0.233 -1.183 (2.58)*** (1.82)*

EQTA 0.109 -0.107 (2.86)*** (-0.75) LCSTF 0.104 -0.637 (-5.53)*** (-2.20)** LnTA 0.064 0.189 (-1.51) (1.15) LLPCR -0.183 -0.534 (-2.15)** (1.98)** OBS -1.654 -1.718 (-3.46)*** (-1.15)

2 2 0.739 0.678 σ + σ v (a) (4.76)*** (2.42)** 2 σ 0.814 0.794

γ = 2 2 σ + σ v (19.54)*** (4.15)*** Maximum Likelihood 26.471 24.389 LR Test 145.81 281.14 Observations 485 320 Bank-specific factors are : (1) return on assets (ROA) measured by profits before taxes to total assets ; (2) the ratio of equity to assets (EQTA); (3) the ratio of bank’s loans divided by customers and short term funding (LCSTF) ; (4) bank size measured by the log of total assets (LnTA) ; (5) ratio of loan-loss provision to total loans (LLPCR); and (6) ratio of off-balance sheet activities to total assets and off-balance sheet activities (OBS). ***, **, * are significant at 1%, 5%, and 10% significance levels, respectively. a: σ 2 = σ 2 = σ 2 u w

The ratio of loans to customer and short term funding (LCSTF) is statistically significant at the 1% level and negatively related to efficiency for domestic banks (significant at 5% and positively related to efficiency for foreign banks). This ratio shows the relationship between comparatively illiquid assets (i.e. loans) and comparatively stable funding sources (i.e. deposits and other short term funding). Therefore, domestic banks have more liquid assets than foreign banks and, high liquidity means improper management of resources and loss of investment opportunities.

The provision for loan loss ratio (LLPCR) is positive and significant at the 5% level for both domestic and foreign banks. This variable, used to account for credit risk, suggests that banks which provide more loans are expected to incur higher credit risk correlated with cost efficiency. This shows that both banks seem to have adopted a risk-averse strategy, mainly through policies that improve screening and credit risk monitoring.

Bank Efficiency of All French Banks

To determine whether the two samples could be pooled in estimating the cost function, we assume that efficiency differences between banks are determined by bank specific rather than technological differences. This assumption allows us to define a common frontier. Pooling all banks would implicitly assume that efficiency differences across banks are attributed, entirely, to managerial decisions within banks. Thus, we use the common frontier approach and we add a dummy variable (Foreign) to compare the domestic and foreign banks of the French banking industry.

Utilizing the normalized version of the translog cost function and the inefficiency estimation procedure in Equation 2, inefficiency scores for the sample banks were calculated. Table 3 presents the results of (weighted) average efficiency in cost of domestic and foreign banks, as well as the total for each of the years of the period analyzed 2000-2007.

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Table 3: Cost Efficiency Scores by Ownership Type and Year

Cost Efficiency Scores (%)

Years Domestic Banks Foreign Banks

2007 68.50 84.25 2006 65.81 87.29 2005 66.88 83.94 2004 66.63 88.33 2003 63.66 88.33 2002 63.16 87.09 2001 61.27 84.56 2000 62.85 84.13

Average scores 64.76 85.95 This table shows cost efficiency scores by ownership type and year for banks that operate in France.

The comparison of domestic banks and foreign banks shows higher efficiency levels in the foreign banks for all sample years. The average cost efficiency score of the 62 examined domestic banks is 64.76 percent. This suggests that, on average, about 34.24 percent of bank resources are wasted. Whereas, the average cost efficiency level for 40 foreign banks is 85.95 percent. This implies that on average 14.05 percent of the resources are wasted. We find that cost efficiency level has increased over the period for domestic banks, except for 2006, and the highest average efficiency level was reached in 2007 (68.50 percent). Foreign banks have improved their cost efficiency since 2000, with decreases of 4% and 3.5% in 2005 and 2007, respectively. This finding contradicts previous empirical literature results that conclude, on advantage, cost efficiency advantages for domestic banks in developed countries such as France (Berger et al. 2000). However, during the last decade, the number of foreign banks in France had increased until 2000, while the number of domestic commercial banks significantly decreased. In part, the number of foreign banks has increased because of the deterioration of the cost efficiency of domestic banks, which allowed foreign banks to increase their market share. Weill (2006b) indicates that reduced performance of French banks allow foreign banks to settle easily in France. The maximum likelihood parameter estimates of model 2 for the cost efficiency are presented in table 4. σ 2 The estimate for the variance parameter γ = 2 2 (γ = 0.914 ) is close to one, which indicates the σ + σ v inefficiency determinants are likely to be highly significant in the analysis of the cost function value. The value of likelihood-ratio test of null hypotheses LR, that the inefficiency effects are absent or that they have simpler distributions is equal to 124.41 for cost efficiency and accepted at the 1% level of significance. This indicates that the joint effect of these explanatory variables on the inefficiencies is significant.

Foreign ownership, measured by the dummy variable Foreign, has a negative and significant coefficient at the 1% level, which indicates that it has a positive effect on the cost efficiency. This result shows that foreign banks are on average more cost efficient than domestic banks in France. This finding goes against previous empirical literature, concluding on advantage of cost efficiency for domestic banks in developed countries such as France (Berger et al. 2000). Therefore, the deterioration of the cost efficiency of domestic banks, allows foreign banks to increase their market share and to settle easily in France.

The ROA coefficient is positive and significant at the level of 5% in the cost inefficiency model, which indicates that it has a negative effect on the cost efficiency. We find a poor quality of management of domestic banks that affects the cost efficiency of French banks. This is consistent with the notion that bad managers are poor at both operations and risk management.

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Table 4: The Effect of Bank-Specific Variables on Bank Inefficiency

Variables Coefficient Foreign -2.712 (-6.42)*** ROA 2.189 (2.08)** EQTA 1.177 (2.46)** LCSTF -0.041 (-9.53)*** LnTA 0.064 (-1.51) LLPCR -2.238 (-2.15)** OBS -3.726 (-5.46)*** 2 2 0.678 σ + σ v (4.76)*** 2 σ 0.914 γ = 2 2 (39.54)*** σ + σ v Maximum Likelihood -261.71 LR Test 124.41 Observations 805 Bank-specific factors are: (1) a foreign ownership variable is a dummy variable equals 1 for foreign banks and equals 0 for domestic banks (Foreign); (2) return on assets measured by profits before taxes to total assets (ROA); (3) the ratio of equity to assets (EQTA); (4) the ratio of bank’s loans divided by customers and short term funding (LCSTF) ; (5) bank size measured by the log of total assets (LnTA); (6) ratio of loan-loss provision to total loans (LLPCR) and (7) ratio of off-balance sheet activities to total assets and off-balance sheet activities (OBS) ***, **, * are significant at 1%, 5%, and 10% significance levels, respectively.

The equity position of a bank has a negative and significant effect at the 5% level on cost efficiency (i.e. positive effect on inefficiency). Indeed, financial capital affects costs through its use as a source of financing loans (Berger & Mester, 1997), and raising capital through issuing shares involves higher costs than taking deposits, so a negative relationship between EQTA and efficiency is expected. As a result, French banks invest more in risky assets. This result is confirmed by the coefficient sign of the OBS variable, which has a positive and significant effect on cost efficiency. Diversification risk does appear to be consistently related to bank efficiency, when a bank is heavily using derivative contracts, such as swaps, forwards, and futures.

The ratio loan-to-deposits (LCSTF), considered as a proxy for liquidity risk, has a positive effect on efficiency and is significant at the 1% level for all banks. This might reflect that bank’s loan product is more highly valued than securities, or it could reflect higher market power that exists in loan markets compared to the other product markets in which banks operate.

The provision for loan loss ratio (LLPCR) is positively and significantly correlated with cost efficiency. This variable, used to account for credit risk, suggests that banks that provide more loans are expected to incur higher credit risk. This may be because banks that spent less resources on credit underwriting and loan monitoring appeared to be more cost efficient.

CONCLUSION

The French banking sector provides an interesting context for studying bank efficiency, as it underwent significant changes during the last two decades. Ownership structures have changed radically, as many foreign banks arrived on the French market and new institutions were created to specialize in specific business lines and types of financing. A subsequent change in the French banking industry was a substantial decrease in the population of banks. At the same time, the number of foreign owned banks in France rose between 1984 and 2001, where foreign institutions have a strong presence.

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In the present paper, we investigate the efficiency of French banks during the period 2000–2007 and analyzed the determinants of banking efficiency in France. We show that foreign banks exhibit higher cost efficiency than their domestic peers. This suggests that foreign banks are better managed in terms of cost efficiency. On the other side, analysis of the determinants of banking efficiency in France suggests a poor quality of management which affects the cost efficiency of French banks that invest more in risky assets.

This study could be extended in several ways. One might use Data Envelopment Analysis (DEA) to compare the two methodologies. It is also interesting to investigate the profit efficiency of the French banking sector.

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BIOGRAPHY

Raoudha Béjaoui Rouissi is an Associate Professor of Finance at High Institute of Accounting and Business, University of Manouba, Tunisia. Her research interests are banking, market efficiency and risk management. Institutional affiliation: Institut Supérieur de Comptabilité et d’Administration des Entreprises (ISCAE), Université de la Manouba E-mail address: [email protected]. Postal address: ISCAE, Campus Universitaire de Manouba, 2010, Tunisia

Houssam Bouzgarrou is currently an Assistant Professor of Finance at Rennes 1 University. He is a researcher at CREM Rennes (UMR 6211 CNRS). His research interests are Mergers and Acquisitions, Corporate governance and Investment Banking. E-mail address: [email protected].

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ANALYSIS OF EXTREME DEPENDENCE BETWEEN ISTANBUL STOCK EXCHANGE AND OIL RETURNS Gözde Ünal, Bogazici University Derya Korman, Bogazici University

ABSTRACT

In this study, the relationship between oil price movements and Turkish stock market is investigated. Given the fact that Turkey is an emerging and oil dependent country, we analyze how the stock market behaves together with the fluctuations in oil prices. The study focuses on extreme observations and uses bivariate extreme value methodology in order to analyze the dependence structure between oil and stock market (ISE100). The residuals of autoregressive integrated moving average (ARIMA) models of stock market index and Brent oil returns are examined by using bivariate extreme value analysis over the period between 1988 and 2011. The overall period studied is analyzed by subdividing the period into two phases. We observe a higher dependence in the second phase (2000-2011), compared to the first phase (1988-1999). Our results show that in the second phase the extremes on the negative tails coincide more commonly compared to the extremes on the positive tails, which is in line with the current literature findings. Our findings suggest diversification opportunities for portfolio managers, as extreme observations in Turkish stock market and oil are asymptotically independent.

JEL: C46, C51, C53, F4

KEYWORDS: Bivariate EVT, Stock Market Returns, Oil Prices, ISE

INTRODUCTION

carce energy sources and world’s growing demand for energy highlight the importance of energy economics. Oil is still the world’s leading energy input considering nearly 35 percent of the global Senergy consumption provided by oil. This percentage is considerably more in the emerging industrialized nations. As it is explained in Basher and Sardosky (2006), emerging economies tend to be more energy intensive and are exposed to higher oil prices compared to developed countries that are more energy efficient in our day. Oil prices fluctuate unprecedentedly during the past decade, especially after 2003. Growing demand in emerging countries, invasion of Iraq, oil price speculations and global financial crises are the main reasons for past decade fluctuations. There is an increasing trend in the oil prices after 2003. Increasing prices have a considerable effect on macroeconomic variables such as growth rate, foreign trade balance and inflation particularly for emerging countries.

This study investigates dependence between oil prices and Turkish stock market. Turkey, as an emerging country, supplies more than 40 percent of its energy requirements from crude oil. About 9 percent of Turkey’s total imports consist of crude oil. Considering importance of oil in Turkish economy, one can say that oil prices have a decisive effect on macroeconomic indicators. Despite various researches done on macroeconomics of energy matter, there is relatively limited number of researches, which concentrate on financial markets’ reaction to the energy prices.

During the last decade, considerable amount of extreme price movements for oil and stock markets are observed. In the volatility of our times, extreme price movements become a familiar phenomenon. Oil and stock market relationship examined by the current literature mainly focused on analyzing central observations. This study concentrated on oil price and Turkish stock market relationship on the extreme events. This approach may help to understand the behavior of financial markets in volatile conditions and

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times of crisis. The rest of the paper continues as follows: Section 2 presents the literature review, Section 3 identifies the model and the data used for this study, Section 4 present the empirical results for the study and Section 5 concludes the paper.

LITERATURE REVIEW

Numerous researchers through the last two decades have studied oil price effect on macroeconomic variables. Hamilton’s (1983) study can be considered as a starting point in the literature of this subject, followed by other researchers such as; Loungani (1986), Gisser and Goodwin (1986), Mork (1989). Considering the number of researches that have analyzed oil price effect, there is relatively small number of works that focused on the stock markets.

Going through the literature, many of the findings represent a negative relationship between oil prices and stock markets. Jones and Kaul’s (1996) study was the first to reveal negative impact of oil prices on stock exchanges. Sadorsky (1999) and Papapetrou (2001) find a negative relationship between stock markets and oil prices. Sadorsky (1999) also reports a change in oil price dynamics, that after 1986 oil price explains a larger fraction of forecast error variance in stock returns. An increased number of researches can be observed after 2000’s together with more diversified findings on oil and stock market relationship.

In the recent literature, some of the negative oil and stock market relationship findings continue as follows: Hammoudeh and Li (2005) reveal a negative bidirectional dynamic relationship between oil future price and Morgan Stanley Capital International (MSCI) global equity index. Park and Ratti (2008) apply multivariate vector auto regressive (VAR) analysis on US and 13 European countries stock indices and conclude that oil price shocks have a negative impact on real stock returns except for Norway, which is an oil exporter country. Bhar and Nikolova (2010) conduct bivariate exponential general autoregressive conditional heteroskedastic (GARCH) model on weekly data and identify a negative time varying conditional correlation between Russian equity market and oil price. Filis (2010) observes negative effect of oil price on Greek stock market by using a VAR approach. Lee and Chiou (2011) find a significant negative impact of oil prices on US stock returns.

Recent findings also show positive relationship between oil and stock markets. Constantinos et al. (2010) estimate a VAR model with granger causality tests on daily data and indicate a significant positive association between Greek stock market and oil prices. Choi and Hammoudeh (2010) employ Markow- switching GARCH models on oil price and US SP500 index and argue that high volatility regimes have positive probability correlations. Zhu et al. (2011) investigate on 14 countries and conclude that increased oil prices have a positive impact on stock prices and increased stocks influence crude oil positively. Narayan and Narayan (2010) conduct a long-run model on daily data and show that oil price and exchange rate have a significant positive effect on Vietnamese stock prices. Basher and Sadorsky (2006) also reveal that oil price risk on 21 emerging stock markets is statistically significant and positive in most models. A part of the literature suggests that there is a conditional relationship between oil and stock market or no relationship at all. Filis et al. (2011) employ dynamic conditional correlation GARCH (DCC-GARCH) model on monthly data of three oil importing and three oil exporting countries.

They assert that precautionary demand side oil shocks cause negative correlations whereas aggregate demand side shocks cause positive correlations. Their findings also show that oil price shocks during global business cycle fluctuations have a significant effect on oil price relationship regardless of countries oil dependence status, however oil shocks caused from production cuts do not seem to have an significant impact. Faff and Brailsford (1999) conduct augmented market model on 24 Australian industry portfolios and oil price. Their findings indicate a positive sensitivity for diversified industrial resources and oil and gas portfolios together with a negative sensitivity for transportation and paper and packaging portfolios. Eryigit (2009) applies Faff and Brailsford’s (1999) augmented market model on 16 sector indices of

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Istanbul Stock Exchange and demonstrate that different oil price effects for different indices. Malik and Hammoudeh (2007) estimate a multivariate GARCH model with BEKK parameterization on daily data and conclude that Gulf equity markets receive volatility from oil market. Mohanty et al. (2010) and Laopodis (2011) analyze European stock markets and reveal that there is no relation between equity and oil prices. Hearn and Man (2010) apply a VAR model on monthly data for China and Hong Kong stock indices and conclude that there is general lack of long-term price integration between markets and oil price. Maghyereh (2004) and Al-Fayoumi (2009) investigate on emerging market stock indices and document that oil price do not affect these stock markets. Hammoudeh and Choi (2006) estimate a VEC model on 5 Gulf Cooperation Council (GCC) countries stock markets and could not find a relationship between oil price and GCC equities. Huang et al. (1996) investigate US SP500 stock index on different levels by using a VAR approach. They provide that there is no relationship between oil futures and broad based stock index but on the firm level they discover a significant relationship.

Contribution of this study to the existing literature will be using bivariate extreme value theory for analyzing the dependence structure of stock market and oil prices. As it is mentioned in some of the studies such as: Choi and Hammoudeh (2010) and Lee and Chiou (2011), different volatility regimes have different oil price effect on stock markets. High volatility environment has many extreme events in term of oil price and stock market movements, which also illustrates our global market structure in the last decade. Instead of the normal price behavior of crude oil, this study will focus on extremal events by using a bivariate extreme value methodology on an emerging market, Turkey.

METHODOLOGY

Data

In this study, daily data is used for crude oil prices and stock market index. The daily data covers the period from January 1988 to August 2011. Brent oil prices are used as the crude oil prices since Brent oil index is the main indicator for Turkish oil trade. Oil price data come from U.S. Energy Information Administration (EIA). Istanbul Stock Exchange 100 (ISE 100) closing prices are used as the stock market data. Stock market data is taken from Istanbul Stock Exchange web site (www.ise.org). Both oil and stock prices are expressed in US dollars. Our data set consists of log returns of spot prices and there is a total of 5732 observation excluding missing days for oil price and stock index. Considering the chronological events of late 1990s and 2000s such as; (1998) Asian economic crisis, (2001) 9/11 attacks, (2003) Iraq war and 2007 Subprime crisis, we divide our dataset into two phases. The first phase covers the years 1988-1999 and the second phase covers the years through 2000-2011. The data covers a period of 23 years during which we observe a shift in scale of prices and volatility both in oil and stock market.

Descriptive statistic results provided in Table 1 demonstrate ISE 100 index returns are more volatile compared to oil returns. Results of the Jarque-Bera test shows that oil returns and ISE 100 index returns are not normally distributed at both phases. Skewness and excess kurtosis values also indicate the same result. Augmented Dickey-Fuller (ADF) test shows us that our data set at both phases are stationary. Data set used in this study is not independent and identically distributed (i.i.d.) except for oil returns at the second phase according to Ljung-Box test statistics. To test data with bivariate extreme value model, data set needs to be converted into i.i.d. series. We utilized an ARIMA model for data conversion process. Data set is examined according to Akaike information criteria (AIC) and Bayesian information criteria (BIC) to get the best-fitted model possible. Tools for selecting AIC and BIC values are provided in Hyndman’s (2011) package ‘forecast’.

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Table 1: Descriptive Statistics

Statistics Phase 1 Phase 2 Brent ISE 100 Brent ISE 100 Mean 0.0001 0.0002 0.0006 0.0003 Median 0.0000 -0.0002 0.0014 0.0008 Maximum 0.1733 0.1774 0.1813 0.2500 Minimum -0.3612 -0.2538 -1.1989 -0.2367 Std. Dev 0.0238 0.0330 0.0249 0.0313 Skewness -1.113 -0.3979 -0.3594 0.0169 Kurtosis 27.653 7.450 8.311 10.421 Jarque-Bera 73,167*** 2,440.7*** 3,430.6*** 6,575.7*** Augmented Dickey-Fuller -12.686*** -12.727*** -12.756*** -11.795*** Ljung-Box 6.714*** 66.391*** 0.751 16.662*** [p-value] [0.0096] [0.0000] [0.3862] [0.0000] Observations 2866 2866 2866 2866 This table shows descriptive statistics for Brent oil log-returns and ISE 100 index log-returns for the two phases. Phase 1 and phase 2 cover the periods from 1988 to 1999 and from 2000 to 2011, respectively. In each phase 2866 daily observations are used, as the extreme value methodology requires high frequency data. ***, **, * Denote significance at the 1%, 5%, and 10% levels, respectively.

Statistical results for Ljung-Box tests and lag order of ARIMA models are represented in Table 2. Ljung- Box results indicate that data set is i.i.d. after fitting ARIMA models. Since oil returns at the second phase are already i.i.d. series, it is not necessary to fit the model for this series.

Table 2: ARIMA Models

Statistics Phase 1 Phase 2 Brent ISE 100 Brent ISE 100 ARIMA Model ARIMA(1,0,4) ARIMA(2,0,2) ARIMA(0,0,0) ARIMA(0,0,1) Ljung-Box 0.0012 0.0010 0.7507 0.0071 [p-value] [0.9718] [0.9742] [0.3862] [0.9329] This table shows fitted ARIMA models and Ljung-Box statistics for oil log-returns and ISE 100 index log-returns for the two phases. Phase 1 and phase 2 cover the periods from 1988 to 1999 and from 2000 to 2011, respectively. In each phase 2866 daily observations are used, as the extreme value methodology requires high frequency data. Data set is examined according to AIC and BIC criteria to get the best-fitted model possible. ***, **, * Denote significance at the 1%, 5%, and 10% levels, respectively.

Model

Bivariate extreme value methodology is used in this study to investigate the dependence between stock market and oil returns. Extreme value method is used to block extrema or exceedances to a predetermined threshold. Determining the threshold level is crucial for extreme value analysis. A low threshold level would cause selecting samples from central part of the distribution, while a high threshold level would eventuate with insufficient data and inaccurate estimates. Threshold level for our data series is determined as 10th percentile for the lowest returns and 90th percentile for the highest returns. Threshold levels are determined visually by using threshold choice plots and mean residual life plots provided in Figure 1, Figure 2 and Figure 3. The dependence strength between extreme returns of ISE and oil prices is estimated by fitting joint exceedances to a bivariate extreme value distribution. Censored likelihood methodology is used for this procedure, which is described in Ledford and Tawn’s (1996) study.Dependence structure of extreme returns is computed by using logistic bivariate Generalized Pareto Distribution (GPD) model. This model is described in Mendes and Moretti (2002), Klüppelberg (2006) and Onay and Ünal (2011). Tools for computing logistic bivariate GPD model are provided in Ribatet’s (2009) POT package. Summary of the model is presented below.

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Figure 1. Threshold Choice Plots

Phase 1

Right tail of oil returns Right tail of ISE Returns 1 1.2

0.5 0.8 0.4

Shape 0 Shape 0

-0.5 -0.4 0.01 0.02 0.03 0.04 0.05 0.05 0.06 0.02 0.03 0.04 0.05 0.06 0.07 0.08 Threshold Threshold

Left tail of oil returns Left tail of ISE Returns 1 0.8 0.8 0.6

0.6 0.4 0.4 0.2 Shape 0.2 Shape 0 0 -0.2 -0.2 -0.4 0.01 0.02 0.03 0.04 0.04 0.05 0.06 0.02 0.03 0.04 0.05 0.07 0.08 0.09 Threshold Threshold

Phase 2

Right tail of oil returns Right tail of ISE Returns 1.2 1

0.6 0.6 Shape Shape 0.2 0

-0.2 -0.6 0.02 0.02 0.03 0.04 0.05 0.05 0.06 0.02 0.03 0.04 0.05 0.06 0.07 0.08 Threshold Threshold

Left tail of oil returns Left tail of ISE Returns 0.6 0.8 0.4

0.2 0.4 0 Shape Shape -0.2 0 -0.4 -0.4 -0.6 0.02 0.03 0.04 0.04 0.05 0.06 0.07 0.02 0.03 0.04 0.05 0.07 0.08 0.09 Threshold Threshold

The figure presents threshold choice plots for the left and right tails of oil returns and ISE 100 returns. The figure shows how the estimated shape parameter changes with different choices of thresholds. For each threshold level, the estimated parameter is plotted within a 95% confidence interval. The thresholds selected are marked with the longitudinal vertical lines. The estimated shape parameters are expected to be constant after the threshold selected.

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Figure 2. Threshold Choice Plots

Phase 1

Right tail of oil returns Right tail of ISE Returns

0.06 0.02

0.02 -0.02 Scale -0.02 Scale -0.06

-0.06 -0.1 0.01 0.02 0.03 0.04 0.05 0.05 0.06 0.02 0.03 0.04 0.05 0.06 0.07 0.08 Threshold Threshold

Left tail of oil returns Left tail of ISE Returns 0.04 0.06 0.02 0.02 0 Scale Scale -0.02 -0.02

-0.04 -0.06 0.01 0.02 0.03 0.04 0.04 0.05 0.06 0.02 0.03 0.04 0.05 0.07 0.08 0.09 Threshold Threshold

Phase 2

Right tail of oil return Right tail of ISE Returns 0.04 0.12

0 0.06 Scale -0.04 Scale 0

-0.08 -0.06 0.02 0.02 0.03 0.04 0.05 0.05 0.06 0.02 0.03 0.04 0.05 0.06 0.07 0.08 Threshold Threshold

Left tail of oil returns Left tail of ISE Returns 0.06 0.09

0.02 0.05 Scale -0.02 Scale 0.01

-0.06 -0.03 0.02 0.03 0.04 0.04 0.05 0.06 0.07 0.02 0.03 0.04 0.05 0.07 0.08 0.09 Threshold Threshold The figure presents threshold choice plots for the left and right tails of oil returns and ISE 100 returns. The figure shows how the estimated scale parameter changes with different choices of thresholds. For each threshold level, the estimated parameter is plotted within a 95% confidence interval. The thresholds selected are marked with the longitudinal vertical lines. The estimated scale parameters are expected to be constant after the threshold selected.

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Figure 3. Mean Residual Life Plots

Phase 1

Right tail of oil returns Right tail of ISE Returns

Mean ExcessMean Mean ExcessMean 0.01 0.02 0.03 0.010.01 0.02 0.03 0.04 0.02 0.03 0.04 0.04 0.05 0.06 0.02 0.03 0.04 0.05 0.06 0.07 0.08 Threshold Threshold

Left tail of oil returns Left tail of ISE Returns

Mean ExcessMean ExcessMean 0.01 0.03 0.05 0.01 0.02 0.03 0.04 0.04 0.05 0.06 0.0150.02 0.03 0.03 0.045 0.04 0.05 0.06 0.07 0.08 Threshold Threshold

Phase 2

Right tail of oil returns Right tail of ISE Returns 0.07

Mean ExcessMean Mean ExcessMean 0.01 0.03 0.05 0.010.02 0.02 0.02 0.03 0.03 0.04 0.04 0.05 0.06 0.02 0.03 0.04 0.05 0.05 0.06 0.07 Threshold Threshold

Left tail of oil returns Left tail of ISE Returns

Mean ExcessMean Mean ExcessMean 0.01 0.02 0.03 0.04 0.02 0.03 0.04 0.05 0.06 0.0150.02 0.03 0.03 0.045 0.04 0.05 0.06 0.07 0.08 Threshold Threshold

The figure represents Mean Residual Life plots for the left and right tails of oil returns and ISE 100 returns. The figure shows how mean value of exceedances over the threshold changes with different choices of thresholds. For each threshold level, the estimated mean residual life value is plotted within a 95% confidence interval. The thresholds selected are marked with the longitudinal vertical lines. The mean excess values against threshold levels are expected to be linear after an appropriate threshold level.

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Following dependence function defines the logistic model:

, (1)

where 0 < α ≤ 1. This gives the joint distribution function:

(3)

for x, y > 0. Complete dependence is obtained when α → 0 and total independence is when α = 1.Dependence of extreme returns between oil and ISE 100 index is identified by the (Chi) statistic of Coles et al. (1999) work.

(3)

Perfect dependence is denoted by -statistic getting closer to 1 and independent variables denoted by -statistic getting closer to 0.

EMPIRICAL RESULT

Threshold levels of 10th and 90th quantiles of oil returns and ISE 100 returns indicates there are 287 highest and 287 lowest extreme events exceeding selected thresholds out of 2865 daily returns for each phase. The corresponding quantiles, taken as thresholds for the analysis of extreme ISE returns, are - 3.41% for the left tail and 3.71% for the right tail in the first phase. In the second phase, the thresholds are -3.29% and 3.21% for the left and right tails, respectively. For Brent returns, 10th and 90th quantiles are 2.38% and 2.29% in the first phase and -2.88% and 2.75% in the second period. For example, if there is a daily loss greater than 3.41% in the first phase studied, the observation is considered to exceed the threshold and is incorporated in the estimation of the GPD model for the left tail. Similarly, if there is a daily return higher than 3.71% in the first phase, the observation is included in the estimation of the model for the right tail. Table 3 shows bivariate EVT model results for ISE100 and Brent oil extreme returns. Independence assumption suggests that 28.6 events coincide on the same day at 10th and 90th quantiles. Between 1988 and 1999, our results show that 30 of the 287 highest returns and 31 of the lowest 287 returns happen on the same day. However, between 2000 and 2011, higher numbers of joint exceedances occurred, as 45 highest returns and 55 lowest returns happen on the same day. In other words, there are 55 days when ISE lost more than 3.29% and Brent oil lost more than 2.88% concurrently.

Table 3 also shows us figures for computing conditional probabilities to investigate oil price latency effect to stock markets by setting a 3-day margin. Under total independence assumption, for each consecutive day 28.6 extreme observations, or a total of 85.8 extreme observations over the next three days is expected. This study shows that 92 of the 287 highest returns (32%) and 85 of the lowest 287 returns (30%) happen within the 3 days at the first phase, while 118 of the 287 highest returns (41%) and 130 of the lowest 287 returns (45%) happens within the 3 days at the second phase respectively. The numbers in parentheses imply conditional probabilities of having an extreme daily stock market return in the coming next three days when today is an extreme day for oil returns. Given that oil loses more than 2.88% one day in the second subperiod, there is 45% probability that in the next three days ISE experiences a daily loss greater than 3.29%.

Table 4 shows the estimated parameters and their standard errors of the GPD models that are fit to our exceedance data over selected thresholds. Except for the second shape parameters for right and left tails in

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the phase 1, all estimated parameters are significant. The alpha parameters of all four models estimated (for both sub periods and for both right and left tails) are close to one, which imply independence in extreme observations. These results are in line with chi-statistic values reported in Table 3, which also imply independence with close to zero values.

Table 3: Bivariate Extreme Model Results for ISE100 and Brent Returns

Phase 1 (1988 -1999) Phase 2 (1999 - 2011) Right Tail Left Tail Right Tail Left Tail

Chi-Statistic 0.017 0.008 0.06 0.103 Deviance 331 412 196 344 Marginal Number Above 287 287 287 287 Joint Number Above 30 31 45 55 Joint Number Above (3days) 92 85 118 130 This table shows the results of the bivariate extreme value models forecasted for Brent oil and ISE 100 extreme returns. The right and the left tail models show results for extreme high returns and extreme high losses returns at 90th quantile respectively. The analysis is carried for two subperiods, where phase 1 corresponds to the period between 1988 and 1999 and phase 2 to the period between 2000 and 2011.

Table 4: Bivariate Extreme Model Estimates for ISE100 and Brent Returns

Phase 1 (1988 - 1999) Phase 2 (1999 - 2011) Right Tail Left Tail Right Tail Left Tail

Scale1 0.0153 (0.0013) 0.0131 (0.0011) 0.0123 (0.0011) 0.0140 (0.0012) Shape1 0.1401 (0.0606) 0.2253 (0.0637) 0.1701 (0.0672) 0.1944 (0.0662) Scale2 0.0212 (0.0016) 0.0242 (0.0020) 0.0152 (0.0014) 0.0204 (0.0018) Shape2 -0.0238 (0.0499) 0.0548 (0.0557) 0.3026 (0.0755) 0.1462 (0.0686) Alpha 0.9880 (0.0105) 0.9942 (0.0118) 0.9560 (0.0159) 0.9238 (0.0174) This table shows the estimated parameters of the GPD models fit using bivariate data series ISE 100 and Brent oil returns at 90th quantile. The numbers in parentheses gives standard errors of the estimated parameters. The analysis is carried for two subperiods, where phase 1 corresponds to 1998 and 1999 and phase 2 to the period between 2000 and 2011.

In general, it is not possible to speak of a dependency relationship between oil and ISE index. Bivariate extreme dependence analysis indicates that oil and ISE 100 returns have higher dependence at the second phase, in the years from 2000 to 2011. Joint number of days exceeding selected thresholds at second phase is increased by 50 percent for the positive tail and by 77 percent for the negative tail compared to the first phase. Chi-statistics at the second phase is 3.5 times greater for the positive tail and 12.9 times greater for the negative tail, compared to the first phase. In the light of model results, increase in the oil and ISE 100 returns extreme dependence during the second phase is clearly mentioned. Negative returns for oil and ISE 100 index have higher dependence value compared to the positive returns at the second phase. It is possible to refer that negative oil price movements affect ISE 100 index more commonly compared to positive movements at the second phase.

CONCLUSION

This paper examines extreme dependence between oil prices and the Turkish stock index. The data used for dependence analysis consist of daily log returns on Brent oil prices and ISE 100 index for the period

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between 1988 and 2011. Turkey supplies nearly half of its energy requirement from crude oil. Oil is one of the major commodities for Turkey’s total imports. Expanding emerging countries need oil as a source of energy for their growing industries. Their exposure to oil price fluctuations is directly compared to developed nations. The main motive of this study is to show an oil price effect on an oil dependent emerging country. Researches that investigate oil price and stock market relationship in the literature, mainly focus on analyzing central observations. This paper is first to study the dependency relationship of stock exchange and oil returns by exploring the extreme observations employing bivariate EVT models.

Results of this study reveal an asymptotic independence between oil prices and ISE 100 index returns in extreme observations. Bivariate extreme dependence analysis applied to the data set by dividing data into two phases, where phase 1 and phase 2 cover the periods from 1988 to 1999 and from 2000 to 2011. In the first phase studied, the chi-statistics are very close to zero (0.008 and 0.017 for the left and right tails respectively), implying no dependence at all. In the second phase, the extreme observations chi-statistics are somewhat higher but still very close to zero (0.103 and 0.060 for the left and right tails respectively). Extreme dependence analysis indicates that oil and ISE 100 returns have higher dependence at the second phase. It is also observed that negative oil price movements affect ISE 100 index more commonly compared to positive movements at the second phase. Oil price effect latency to stock markets is also examined within the study by setting a 3-day margin. Yet, the number of observed joint exceedances is quite close to the expected values under complete independence assumption.

Findings of this study, which indicates absence of extreme dependence between oil and stock markets, may help portfolio managers and investors identify better diversification opportunities. However, considering higher dependence of stock markets for negative oil price movements especially in the last decade, diversification opportunities must be used with caution in the times of crisis.

REFERENCES

Al-Fayoumi, N. A. (2009). Oil Prices and Stock Market Returns in Oil Importing Countries: The Case of Turkey, Tunisia and Jordan. European Journal of Economics, Finance and Administrative Sciences, 16, 84-98.

Basher, S. A. and Sadorsky, P. (2006). Oil price risk and emerging stock markets. Global Finance Journal, 17(2), 224-251.

Bhar, R. and Nikolova, B. (2010). Global oil prices, oil industry and equity returns: Russian experience. Scottish Journal of Political Economy, 57(2), 169-186.

Choi, K. and Hammoudeh, S. (2010). Volatility behavior of oil, industrial commodity and stock markets in a regime-switching environment. Energy Policy, 38(8), 4388-4399. Elsevier B.V.

Coles, S., Heffernan, J. and Tawn, J. (1999). Dependence Measures for Extreme Value Analyses. Extremes, 2(4), 339-365.

Constantinos, K., Ektor, L. A. and Dimitrios, M. (2010). Oil Price And Stock Market Linkages In A Small And Oil Dependent Economy: The Case Of Greece. Journal of Applied Business Research, 26(4), 55-64.

Eryigit, M. (2009). Effects of Oil Price Changes on the Sector Indices of Istanbul Stock Exchange. International Research Journal of Finance and Economics, 25, 209-216.

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Faff. R. W. and Brailsford, T. J. (1999). Oil price risk and the Australian stock market. Journal of Energy Finance & Development, 4(1), 69-87. Elsevier B.V.

Filis, G. (2010). Macro economy, stock market and oil prices: Do meaningful relationships exist among their cyclical fluctuations? Energy Economics, 32(4), 877-886. Elsevier B.V.

Filis, G., Degiannakis, S. and Floros, C. (2011). Dynamic correlation between stock market and oil prices: The case of oil-importing and oil-exporting countries. International Review of Financial Analysis, 20(3), 152-164. Elsevier B.V.

Gisser, M. and H Goodwin, T. (1986). Crude Oil and the Macroeconomy: Tests of Some Popular Notions: Note. Journal of Money, Credit and Banking, 18(1), 95-103. Ohio State University Press.

Hamilton, J. D. (1983). Oil and the Macroeconomy since World War II. Journal of Political Economy, 91(2), 228.

Hammoudeh, S. and Li, H. (2005). Oil sensitivity and systematic risk in oil-sensitive stock indices. Journal of Economics and Business, 57(1), 1-21.

Hammoudeh, S. and Choi, K. (2006). Behavior of GCC stock markets and impacts of US oil and financial markets. Research in International Business and Finance, 20(1), 22-44.

Hearn, B. and Man, S. Y. (2010). An Examination of Price Integration Between Stock Market and International Crude Oil Indices: Evidence from China. Applied Economics Letters, Forthcoming. Available at SSRN: http://ssrn.com/abstract=1724303

Huang, R. D., Masulis, R. W. and Stoll, H. R. (1996). Energy shocks and financial markets. Journal of Futures Markets, 16(1), 1-27. John Wiley & Sons.

Hyndman, R. J. (2011). A users guide to the ‘forecast’ package, (Version 3.11), http://cran.r- project.org/web/packages/forecast/index.html

Jones, C. M. and Kaul, G. (1996). Oil and the Stock Markets. Journal of Finance, 51(2), 463.

Klüppelberg, C. and May, A. (2006) Bivariate extreme value distributions based on polynomial dependence functions. Mathematical Methods in the Applied Sciences, 29, 1467–1480.

Laopodis, N. T. (2011). Equity prices and macroeconomic fundamentals: International evidence. Journal of International Financial Markets Institutions and Money, 21(2), 247-276. Elsevier.

Ledford, A. W. and Tawn, J. A. (1996). Statistics for near independence in multivariate extreme values. Biometrika, 83(1), 169-187.

Lee, Y.-H. and Chiou, J.-S. (2011). Oil sensitivity and its asymmetric impact on the stock market. Energy, 36(1), 168-174. Elsevier Ltd.

Loungani, P. (1986). Oil price shocks and the dispersion hypothesis. The Review of Economics and Statistics, 68(3), 536–539. JSTOR.

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Maghyereh, A. (2004). Oil price shocks and emerging stock markets: A generalized VAR approach. International Journal of Applied Econometrics and Quantitative Studies, 1(2), 27–40. Euro-American Association of Economic Development.

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Mohanty, S., Nandha, M. and Bota, G. (2010). Oil shocks and stock returns: The case of the Central and Eastern European (CEE) oil and gas sectors. Emerging Markets Review, 11(4), 358-372. Elsevier B.V.

Mork, K. A. (1989). Oil and the Macroeconomy When Prices Go Up and Down: An Extension of Hamiltonʼs Results. Journal of Political Economy, 97(3), 740-744. The University of Chicago Press.

Narayan, P. K. and Narayan, S. (2010). Modelling the impact of oil prices on Vietnam’s stock prices. Applied Energy, 87(1), 356-361.

Onay, C. and Ünal, G. (2011) Cointegration and Extreme Value Analyses of Bovespa and Istanbul Stock Exchanges. Czech Journal of Economics and Finance, Forthcoming. Available at SSRN: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1636183

Park, J. and Ratti, R. (2008). Oil price shocks and stock markets in the U.S. and 13 European countries. Energy Economics, 30(5), 2587-2608.

Papapetrou, E. (2001). Oil price shocks, stock market, economic activity and employment in Greece. Energy Economics, 23(5), 511-532. Elsevier.

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Zhu, H., Li, S. and Yu, K. (2011). Crude oil shocks and stock markets: A panel threshold cointegration approach. Energy Economics, 33(5), 987-994. Elsevier B.V.

ACKNOWLEDGEMENT

This research is supported by Bogazici University Research Fund (D6300).

BIOGRAPHY

Gözde Ünal is an Instructor of Finance at Boğaziçi University, Istanbul. Prior to her academic career she also worked in the Corporate Finance field. Her research appears in journals such as Journal of Risk Model Validation, Finance a úvěr (Czech Journal of Economics and Finance). She can be reached at Boğaziçi University, Bebek 34342 Istanbul/Turkey, [email protected]

Derya Korman is graduate student in International Trade Management at Bogazici University. He is also works as a trade coordinator at Derin Shipping, Transport and Trading Ltd., Istanbul. He can be reached at Boğaziçi University, Bebek 34342 Istanbul/Turkey, [email protected], [email protected].

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INTEGRATION OF KEY WORLDWIDE MONEY MARKET INTEREST RATES AND THE FEDERAL FUNDS RATE: AN EMPIRICAL INVESTIGATION Krishna M. Kasibhatla, North Carolina A&T State University

ABSTRACT

This study investigates whether there is an increased integration of U.S. domestic money market interest rates and the Eurodollar market interest rates following two important changes that the U.S Federal Reserve (the Fed) implemented. First, elimination of reserve requirements on Eurodollar bank deposits in the early 1990s. Second, change in the operating procedure for conducting monetary policy in early 1992 from borrowed reserves targeting to federal funds rate targeting. The money market interest rates are three and six month Eurodollar London Interbank Offered Rates (Libor), three and six month U.S. Treasury bill (T-bill) rates, and the effective federal funds rate. Cointegration and error-correction methodology of Johansen and Juselius (1990,1992) is employed for this empirical study. Results indicate that integration of the five interest rates increased following the two changes by the Fed. It is the effective fed funds rate and the three-month T-bill rate that participate in the adjustment process back to their equilibrium path following an external shock to the system. Granger causality tests produced different and somewhat conflicting results when the error-correction model is estimated with and without the federal funds rate in the system. This finding requires further study and investigation.

JEL: C32, C58, E43, G15

KEYWORDS: Libor, Unit Root, Cointegration, Granger Causality

INTRODUCTION

he primary objective of this study is to examine whether there is an increase in the integration of U.S. domestic money market interest rates and the offshore Eurodollar London interbank offered Trates (Libor) since 1990 following two important changes made by the U.S. Federal Reserve (the Fed). First, a regulatory change eliminating reserve requirements on Eurodollar liabilities of offshore banks in early 1990, and second, change in the Fed’s operating procedure for conducting monetary policy switching from borrowed reserve targeting to federal (fed) funds rate targeting in early February, 1992. The money market interest rates used in this study are the three and six month U. S. Treasury bill (T-bill) rates, effective fed funds rate, and the offshore three and six-month Libor. To my knowledge there is no published study that exclusively investigated the relationship between the domestic and offshore rates after the monetary policy regime change to fed funds targeting in early 1992.

The relationship between U.S. T-bill yields including the effective fed funds rate and the Libor of different maturities is examined employing cointegration and error correction methodology developed by Johansen and Juselius (JJ) (1990, 1992). The choice of methodology to examine the relationships is supported by several similar studies summarized in Hall, Anderson, and Granger (1992).

These money market rates play a very important role in influencing various other interest rates. For instance the three month T-bill rate is a prominent default-risk-free rate in the U.S. and often used as a proxy for a risk-free asset as well as a benchmark lending rate. Likewise, according to some estimates, the value of financial contracts linked to the Libor is over $3 trillion. The Eurodollar market and the fed funds market are located in different places but the currency in both the markets is the U.S. dollar. The volume of transactions in the Eurodollar interbank market is larger than the U.S. fed funds market. Further,

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according to Baba et al (2009) European banks had substantially increased their U.S. dollar asset positions from about $2 trillion to over $8 trillion by mid-2007. The effective federal funds rate is considered a ‘bellwether’ rate leading all short-term rates. The effective fed funds rate is the weighted average of the funds rates that prevailed during the day. The weights are the amounts of funds traded at different rates during the day.

During the financial and banking crises in the U.S. from late 2007 to mid-2009 the international money markets, especially, the interbank Eurodollar market, ran into serious trouble due to shortage of U.S. dollars. According to McAndrews et al (2008) the volume of transactions in the interbank market sharply declined and banks reportedly could not borrow funds at the posted rates. These interbank loan rates rose to very high levels, and spreads between the three-month Libor and the effective fed funds rate suddenly jumped to over 90 basis points, from an average of 20 basis points. The increased spread was mainly attributed to increased liquidity and credit risks of banks. Banks sustained huge losses due to the collapse of the mortgage market in the U.S. McAndrews et al (2008) reported that on December 12, 2007, the Fed responded to the shortage of term funds for banks by introducing the ‘Term Auction Facility’ (TAF) that provided term funding to eligible banks through periodic auctions. According to Taylor and Williams (2008) as many private loans are linked to Libor rates, the sharp increase in these spreads raised the cost of borrowing and interfered with monetary policy. The widening spreads became a major focus of the Federal Reserve which took several actions including the TAF.

The foreign-currency exposures of European banks had grown significantly over the decade preceding the crisis, with dollar exposures accounting for half of the growth in European banks’ foreign exposures over the period from 2000 to 2007 (McGuire and von Peter 2009a). European Union, United Kingdom, and Swiss banks’ on-balance sheet dollar exposures were estimated to exceed $8 trillion in 2008.While there was severe shortage of term funds for banks, there was a lot of concern about the alleged manipulation of Libor in 2008, and according to the financial press reports several participating banks were accused of conspiring to artificially keep the Libor rates low. If these accusations are proven to be valid, then we cannot expect the key rates to maintain an equilibrium relationship if Libor manipulated rather than market determined.

An understanding of the relationship among these key market rates and rates targeted by central banks is of great importance for market participants as well as the monetary authorities in implementing monetary policy. To my knowledge, there is no study which examined all the five rates simultaneously. The results of this study provide much needed insight into the interactions between monetary policy and key money market rates in the U.S. and Eurodollar market. The remainder of this paper is organized as follows. The following section contains literature review. Section two presents the data and an overview of the methodology. Section three contains the estimated results and discussion. Section four contains summary and concluding remarks.

LITERATURE REVIEW

The literature on the integration of key domestic and international market interest rates is extensive. This literature review includes a select group of studies widely cited. These studies are divided into two categories. The first category of studies, which I refer to as early studies, mainly focused on testing for causal links among the domestic and foreign interest rates. The second category of studies was mainly interested in testing the ‘expectations hypothesis’ of the term structure of interest rates, besides examining their short-term dynamics. Expectations hypothesis is that long-term interest rate is the average of the current short-term rates and expected future short-term rates plus a constant term premium.

The following studies by Hartman (1984), Swanson (1987, 1988), Fung and Isberg (1992), Mougoue and Wagster (1997) belong to the first category, that tested for temporal causality between U.S. domestic

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interest rates Eurodollar rates. All of these studies used U.S. domestic certificate of deposit (CD) rates, weekly or daily frequency, and either one month or three month Eurodollar deposit rates in their studies, except Kaen and Hachey (1983), whose study involves the relationship between Eurodollar rates and the Euro-sterling rates. The sample period of Hartman’s (1984) study ranges from 1971-1978, divided into two sub-periods, 1970-1974 and 1975-1978. The study reported unidirectional causality running from the U.S. domestic CD rates to the 3-month Eurodollar rates for the 1970-1974 period, and bidirectional or mutual causality for the 1975-1978 period. Swanson (1987, 1988) used daily CD rates to test for Granger causality for the sample period from mid-1973 to December 1984, split the sample into three sub-periods. For the first sub-period (1974-1980) she reported unidirectional causality (note, Hartman study reported bidirectional causality for this period). And for the sub-period 1981-1982 her study reported bidirectional causality, and for the sub-period 1982-1983 the finding was reverse unidirectional causality, from the Eurodollar rates to the CD rates.

The study by Fung and Isberg ( 1992) used daily CD rates and employed vector error correction model to test for the causal links for the sample period 1981-1988. For the sub-period 1981-1983 they found unidirectional causality from the U.S. CD rates to the Eurodollar rate. Note, for the same period Swanson study reported reverse unidirectional causality. For the 1984-1988 sub-period they reported bidirectional causality. The study by Mougoue and Wagster (1997) employed daily CD rates and 3-month Eurodollar deposit rates for the sample period from mid-1973 to mid-1993. They argued that the correct way to measure and test for the relationship between the rates should recognize the difference in trading times of these two markets. They did account for this difference in trading times. They divided the sample into three sub-periods, coinciding with the three different monetary policy operating regimes of the U.S. Federal Reserve.

Mougoue and Wagster (1997) identified three different monetary policy regimes the Fed had followed, first, the Fed targeted federal funds rate targeting from July 16, 1973 to October 5, 1979, second, non- borrowed reserve targeting from October 9, 1979 to October 7, 1982, and the third, targeting borrowed reserves from October 8, 1982 to February 7, 1992, in conducting monetary policy. For the three different regimes Mougoue and Wagster (1997) study reported three different causal relations. For the fed funds targeting regime their finding was bidirectional causality, for the non-borrowed reserve targeting causality was reverse unidirectional, from the Eurodollar market to the U.S. domestic market, and for the borrowed reserve targeting causality was unidirectional from the U.S. domestic rates to the Eurodollar rates. The study by Kaen and Hachey (1983) used Eurodollar rates and Euro-sterling rates to test for causality and reported unidirectional causality from the U.S. dollar to the Euro-sterling rate for their entire sample period ranging from 1974 to 1981. We can clearly see the conflicting and contradictory results reported in the above studies. However, one common finding in these studies is that these rates are integrated.

Some of the influential studies in the second category include Engle and Granger (1987), Stock and Watson (1988), Campbell and Shiller (1991), Hall et al (1992), Engsted and Tanggaard (1994), Campbell et al (1997), and Thornton (2002). Empirical support for the ‘expectations hypothesis’ reported in these studies is generally weak. In spite of that all these studies reported that the U.S. and Eurodollar rates co- move in the long-run. However, there is no agreement as to the causal links between the rates examined.

Most recent studies include Clinebell et al (2000), Sarno and Thornton (2003), and Zhou (2007). Clinebell et al (2000) went a step further besides testing whether the financial markets integration increased and examined the changing monetary policy regimes and its impact on Granger causality. That is whether the conflicting results were due to changes in the Fed’s monetary policy targets. The study found strong financial market integration under fed funds rate targeting phase relative to the non- borrowed reserve targeting. Their findings were contradicted by the results reported in the Sarno and Thornton (2003) study. Employing a non-linear error-correction model they reported no change in long- run equilibrium relationship and short-run dynamics between the interest rates irrespective of different

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monetary policy regimes. The study by Zhou (2007) differs from the earlier studies in one respect, namely, in the selection of interest rates. Zhou(2007) chose the federal funds rate and the Eurodollar rates of different maturities. The cointegration tests conducted in this study are bi-variate, not multivariate, even though the number of rates involved are four. Each pair of interest rates are reported to be cointegrated. The interesting aspect of this study is the results of the error-correction model. Specifically, it is the federal funds rate that plays a major role in the adjustment process of the system when it is out of equilibrium following a shock. This is also one of the key findings of this study.

This study differs from the earlier studies, reviewed above, in three important ways. First, this study explicitly includes the effective fed funds rate along with three and six-month Libor and the two domestic three and six-month T-bill rates. One or two of earlier studies included the fed funds target rate, which is not the interbank lending rate, but it is the Federal Reserve’s monetary policy target rate. Second, the sample period in this study is the longest, from January 6, 1986 to September 14, 2009, close to 6,000 daily observations. Time series models estimated using high frequency data as well as a long time periods are expected to perform well in providing more reliable cointegrating rank and coefficient estimates. Finally, this study estimated multivariate cointegration models rather than bi-variate cointegration model as many of the earlier studies. As such, there is very little discussion on the number of identified cointegrating vectors, perhaps, the focus of those studies was testing for the causal linkages between the market interest rates. This study fills the gap by emphasizing the long-run equilibrium relationship rather than just testing for temporal Granger causality. The results of this study provide much needed insight into the interactions between monetary policy and key money market rates in the U.S. and Eurodollar market. The results of this study provide a better understanding of the interactions between monetary policy and key money market rates.

METHODOLOGY

Data

The sample data are the three and six month daily (5-day week) three and six-month T-bill yields, three and six month Eurodollar Libor interbank lending rate, and the effective federal funds rates, for the periods January 6, 1986 to September 14, 2009. The Eurodollar rates are computed by Thomason Reuters for the British Bankers Association. A department of the British Bankers Association (BBA) averages the inter-bank interest rates being offered by its membership. Libor is calculated for periods as short as overnight and as long as one year. Libor daily data series are from the BBA (www.bba.org.uk). The three and six month T-bill rates are the secondary market rates data, and are from the Federal Reserve Bank of St. Louis website (www.stlouisfed.org).

Summary statistics of the five interest rate series are presented in Table 1. The first four moments of the five money market interest rates are provided in Table 1. The effective federal funds rate for the sample period averaged about 44 basis points over 3-month T-bill rate and about 27 basis points over the average 6-month T-bill rate. Average effective fed funds rate is below the 3-month and 6-month Eurodollar rates by 38 and almost 50 basis points, respectively. One explanation for the fed funds rate being higher than the U.S T-bill rates is that the two markets are partially segmented according to Campbell et al (1997). This is contrary to the belief that the fed funds rate affects all other short-term interest rates. Another explanation may be due to the fact that the T-bill rates have no liquidity risk and are default risk free, and may have some tax advantages while fed funds rate is not free of default and liquidity risks, according to Sarno and Thornton(2003). Based on the standard deviations of the rates, the fed funds rate is slightly more variable than the T-bill and the Eurodollar rates. The third and fourth moments show positive skewness and high kurtosis, which is an indication that the underlying distributions of these rates are non- normal. This is confirmed by the Jarque-Bera test for normality (bottom row of Table 1).

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Table 1: Summary Statistics: tb3m, tb6m, lbr3, lbr6m, and ffr

tbm3m tb6m lbr3m lbr6m Ffr Mean 5.3150 5.4848 6.1454 6.2359 5.7581 Median 5.1800 5.3300 5.9375 5.9883 5.5700 Maximum 9.0900 9.1200 10.625 11.000 16.170 Minimum 2.6100 2.7500 3.1250 3.1250 2.5800 Std. Dev. 1.4253 1.3757 1.6352 1.6201 1.7672 Skewness 0.3047 0.2150 0.2194 0.2335 0.3529 Kurtosis 2.7232 2.6859 2.7038 2.7462 3.0938

Jarque-Bera 70.9281 44.9055 44.3878 44.7289 80.2814 Probability 0.0000 0.0000 0.0000 0.0000 0.0000 The first four moments of the five money market sample interest rate series are provided in Table 1. The average effective federal funds rate for the sample period was 44 basis points over the mean 3-month T-bill rate and 27 basis points over the mean 6-month T-bill rate. The mean federal funds rate is below 38 basis points of 3-month average Libor and nearly 50 basis points below 6-month Libor. The rates exhibit positive skewness and a relatively high kurtosis. The distribution of the market interest rates appears to be non-normal.

Model

The relationship between T-bill yields (tb3m and tb6m) and Libor (lbrm3m and lbrm6m) and effective federal funds rate (ffr) is studied within the framework of cointegration and error-correction methodology employing the JJ procedure and the error correction model for examining the short-run dynamics. JJ methodology uses an asymptotically fully efficient maximum likelihood technique for the estimation of cointegrating vectors.

The general form of the time series model underlying the empirical estimation is stated as a k-order Gaussian vector autoregressive (VAR) model for X as: k X t = µ + ∑ Ai X t−i + ε t (1) i=1

where, Xt is a n×1 column vector of observations on the variables of the model, µ is a vector of constants, Ai are n×n matrices of autoregressive coefficients (that do not contain any zero elements), εt is a vector of n non-observable random errors usually assumed to be contemporaneously correlated but not autocorrelated, and k is the number of lags on the variables in the system.

If the variables in Xt are integrated of, say, order one, I(1), and are also found to be cointegrated, that cointegration restriction has to be incorporated in the VAR in (1). The Granger Representation Theorem (Engle and Granger, 1987) states that variables, individually driven by permanent shocks are cointegrated if and only if there exists a vector error correction representation of the time series data. A VAR model, with this restriction embedded is referred to as the vector error-correction (VEC) model. Variables in the VEC model enter the equation in their first differences, and the error correction terms are added to the model. The VEC has cointegration relation built into the specification so that it restricts the long-run behavior of the endogenous variables to converge to their long-run relationship while allowing for short- run dynamics. Deviations from long-run equilibrium are corrected through a series of partial short-run adjustments per unit of time.

The VEC representation of the VAR in (1), following JJ is:

k ∆X t = µ + ∑Γi ∆X t−i + ΠX t−1 + ξt (2) i=1

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where Xt is a n×1 vector of I(1) variables, Γi is a n×n matrix of coefficients of the short-run dynamic effects, Π is a n×n matrix of coefficients of long run effects, and ξt is a vector white noise process. If the rank of Π in equation (2) is r, where r ≼ n-1, then Π can be decomposed into two n×r matrices, α and β, such that Π = αβ′. The matrix β is the cointegrating matrix of r cointegrating vectors, β1, β2, …, βr. The β vector represents the estimate of the long-run cointegrating relationship among the variables in the system. The error-correction terms, β′Xt-1, are the mean-reverting weighted sums of cointegrating vectors. The matrix α is the matrix of error correction coefficients, the so called ‘speed of adjustment’ coefficients that measure the speed at which the variables adjust to their long-run equilibrium values. If the rank of Π

in equation (2) is found to be r ≼ n-1, the above model can be expressed in the first differences of Xt, augmented by the error correction terms, αβ′Xt–1, as shown below:

′ ∆X t = µ + ∑Γi ∆X t−i +αβ X t−1 + ξt (3)

The JJ technique provides maximum likelihood estimates of α and β′. In our model, Xt is a 2×1 vector consisting of T-bill yields, two Libor, and fed funds rate of different maturities. The cointegrating relationship, r, is determined by the trace eigenvalue statistic and the maximum eigenvalue statistic of the stochastic matrix and the maximum likelihood estimates of the cointegrating vectors (β) in equation (3).

The standard estimation process has three-steps. First, unit root tests of each of the interest rate series in levels for their degree of integration. If two or more time series are integrated of the same order and are found to be cointegrated, then, according to the Granger Representation theorem (Engle-Granger, 1987), there must be ‘Granger causation’ at least in one direction. Further, such cointegrated series must be modeled within an ‘error correction’ framework as per Engle and Granger (1987). If the interest rate time series are found to be integrated of the same order in the first step, then, in the second step we have to test for cointegration of the time series to see if there is a long-run equilibrium relationship between the variables concerned. If cointegration is found in the levels of the series, then, the interest rate series should be modeled in the framework of vector error correction procedure described in (3). In all of the above estimation procedures, we use the Schwarz (1978) information criterion (SIC) to determine the lag structure for the unit root tests, cointegration tests, and for estimating the vector error-correction model.

The initial step in the estimation involves the determination of the time series proprieties of each variable individually by conducting two of the popular unit root tests, namely, the augmented Dicky Fuller (ADF) (1979), and the KPSS (Kwiatkowski, Phillips, Schmidt, and Shin, (1992) test. For the KPSS unit root tests the Bartlett kernel spectral estimation and Newey-West (1994) bandwidth selection methods were used. The ADF test involves running one of the following regressions:

Yt = α Xt-1 + Σdi ∆Xt-I (4)

Yt = µ + α Xt-1 + Σdi ∆Xt-I (5)

Yt = µ + βT+ αXt-1 + Σdi ∆Xt-I (6)

The KPSS test differs from the ADF unit root test. In the KPSS test, each e series, Xt, is assumed to be trend- stationary under the null hypothesis. The KPSS statistic is based on the residuals from the OLS regression of Xt on the exogenous variables, Xt: (Xt = Zt′δ + ut). The null hypothesis is different for the ADF and KPSS tests. The null hypothesis for the ADF test is H0: I(1), but for the KPSS test is H0: I(0).

For the ADF test the correct specification of the equation is determined based on the data generating process (DGP). If it is determined that the DGP is a random walk without a drift and a mean, then the unit

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root test is based on equation (4). If the DGP is a random walk with a drift and zero mean, then we have to use equation (5). Equation (6) is appropriate if the series has a non-zero drift and non-zero mean.

RESULTS AND DISCUSSION

The unit root test results for each series, in log levels and in log first differences, are presented in Table 2. The ADF tests, involving each of the time series in log levels, the null of ‘unit root,’ [H0: I(1)], could not be rejected, while the null of ‘no unit root’ [H0 : I(0)], for the KPSS test was rejected. Then, the series are tested in their log first differences. In all the cases, the ADF test rejected the null of ‘unit root,’ in log first differences, and in the KPSS tests, the null of ‘no unit root’ could not be rejected in log first differences of the series (note, the null of the KPSS test is the opposite of ADF test). According to the ADF tests, the five time series are stationary in their log first differences. The KPSS test results confirm the ADF test conclusion. The finding that each one of the interest rates is I(1) is consistent with the results of the studies by Stock and Watson (1988, 1999).

Since the unit root tests indicated that each of the interest rate series is integrated of the same order, I(1), we conducted the cointegration tests on the series employing the JJ procedure. Three multivariate cointegration tests of the interest rates are performed. First, a cointegrating rank test among all the five market interest rate series: ltb3m, ltb6m, llbr3m, llbr6m, and lffr. Then, a cointegration test among the four series: ltb3m, ltb6m, llbr3m,and llbr6m. Finally, a cointegration test between ltb3m, ltb6m, and lffr, the U.S.domestic interest rates. All cointegration tests are performed in log levels of the interest rate series and the equations are normalized on the ltb3m. Test results are presented in Table 3, panels (a), (b) and (c), respectively. In the case of the five interest rate series, panel (a) of Table 3, the trace test identified two cointegrating vectors but the maximum eigenvalue tests identified three cointegrating vectors. Since there is no agreement between the trace and maximum eigenvalue tests, I followed the convention of choosing the trace test results of two cointegrating vectors as more appropriate. Among the five interest rate series I conclude that there are two cointegrating vectors. For the four interest rate series without the fed funds rate, panel (b), Table 3, both trace and maximum eigenvalue tests identified two cointegrating vectors. For the three U.S. domestic rates, panel (c), Table 3, both the trace and maximum eigenvalue tests indicated one cointegrating vector. Tests of residuals from the estimated cointegrating equations indicate that the residuals are stationary in all cases. However, we cannot conclude that the estimated coefficients of the cointegrating equations are structural parameters.

Results of the cointegration test between ltb3m, ltb6m, and lffr , bottom part of panel (c) of Table 3, indicate one cointegrating vector. There is no one-to-one relationship between ltb3m and lffr. The finding of two cointegrating vectors in the bottom panel (a) of Table 3 imply that there are two ways the five interest rate series can be stable and two ways the series can deviate from each other. In general, more cointegrating vectors means more ways the system will be stable. So, there is strong evidence that the two money markets are more integrated during the sample period for this study. Studies in the late 1980s and early 1990s reported one cointegrating vector between fed funds rate and the T-bill rates, between three month T-bill rate and three month Libor, and between one month CD rate and the three month Libor deposit rate. The studies by Clinebell et al (2000) and Sarno and Thornton (2003) also reported one cointegrating vector. However, Clinebell et al (2000) did report any increased integration of the two markets. Their conclusion is exclusively based on no finding of mutual causation between T-bill rates and Libor. Clinebell et al (2000) state that ‘increased integration and more rapid movement of dollar flows should promote bi-directional Granger causality between T-bills and LIBOR’.

The study by Zhou (2007) used bivariate cointegration tests among three different sets of interest series and reported one cointegrating vector in all the three cases. There can only be one cointegrating vector between two variables integrated of the same order, say, I(1). The finding of two cointegrating vectors among the five interest rate series in this study is a strong indication that integration of U.S. domestic and

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Eurodollar market rates has substantially increased during the sample period under investigation. Since we cannot directly infer that the increased integration is due to the elimination of reserve requirements on Eurodollar deposits in 1990 and also due to the switch to fed funds rate targeting in the conduct of monetary policy by the Fed in 1992. However, the increased integration of the two markets may be in large part attributed to the two changes, the regulatory and monetary policy operating procedure.

Table 2: Augmented Dickey-Fuller & KPSS Unit Root Tests

ADF Test1 KPSS Test2 Test Statistic in Test Statistic LM Test Stat Variable levels2 in first diff LM Test Stat in levels in first diff

ltb3m -1.1367 -20.288*** 4.1011 0.2914*** ltb6m 0.5242 -4.6543*** 4.2729 0.5434*** llbor3m -0.9965 -61.757*** 4.3312 0.5264*** llbor6m -0.2018 -25.934*** 4.3312 0.3401*** Lffr -1.0489 -31.989*** 4.7371 0.3553***

The equation estimated is Yt = µ + α Xt-1 + Σδi ∆Xt-i for the ADF test. ADF and KPSS unit rate test results indicate that all the interest rate series in their log levels are I(1), and are stationary in their log first differences. 1 1 % critical value for ADF test with intercept is -3.4313, 5% critical value with intercept is -2.8618, 1%. Critical value with intercept and trend it is -3.9595, and for 5% it is -3.4105. 2 1% critical value for KPSS LM statistic with intercept is 0.739 and 5% value is 0.463, and with intercept and trend the critical values are 0.216 and 0.146, respectively. *** indicates significance at 1% level.

In the four variable system of ltb3m, ltb6m, llb3m, and llb6m, shown in column (b) of Table 3, the maximum number of cointegrating vectors is three. The finding of two cointegration vectors means that there are two ways these rates can be stable. In the case of one cointegrating vector among ltb3m, ltb6m, and lffr, displayed in column (c), there is only one way the system is stable.

The time period from late 2007 to late 2009 is characterized by severe financial crisis in the U.S. During that period there were accusations that the Libor was being manipulated by some of the large participating banks in the Eurodollar market. I used the time period from January 2, 2007 to September 14, 2009 to test whether the accusation of manipulation of Libor had any impact on the cointegrating relationship among the five interest rates. The estimated results indicated two cointegrating vectors among the five interest rate series. For want of space the results are not presented in this paper but are available to anyone interested in these results. However, as the sample period of less than three years is too short to estimate a cointegrating relationship I do not want to conclude that the U.S. financial crisis did not disrupted the degree of integration of the two markets. Instead, I would prefer to examine this issue when the sample period is over five or six years.

For examining the short-run dynamics of the interest rates, two VEC models (equation 3) are estimated, one with all the five rates in their log first differences, and the other with the four money market interest rates (i.e. without the lffr) in their log first differences. Based on the estimated results of the VEC models presented in Table 4, panels (a) and (b), we can draw some key inferences about the adjustment mechanism of the interest rates to their long run equilibrium path following an external shock to the system of interest rates.

The magnitudes and signs of the estimated error-correction coefficients reported in panels (a) and (b) of Table 4 provide information as to how the equilibrium is restored, the direction and magnitude, following an external shock. Results in panel (a) of Table 4 show that the error-correction coefficients of two equations, Δlffr and Δltb3m, are statistically significant. However, the error correction coefficients in the remaining equations are not statistically significant at the conventional levels, which implies that ltb3m and lffr do participate in the adjustment process, but their participation is not statistically significant.

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Table 3: Cointegration Test Results

Panel (a) Panel (b) Panel (c ) H0: r = 0, Ha =1

Trace statistic 893.66*** 866.50*** 252.05*** Critical Value 85.336 61.195 41.195 Max. Eigen Stat 786.91*** 778.52*** 234.69*** Critical Value 40.295 33.733 27.068

H0: r ≤ 1, Ha = 2

Trace statistic 106.75*** 87.981*** 17.353 Critical Value 61.267 41.195 25.078 Max. Eigen Stat. 77.029*** 74.405*** 15.329 Critical Value 33.733 27.068 20.161

H0: r ≤ 2, Ha =3

Trace statistic 29.717 13.576 Critical Value 41.195 25.078 Max. Eigen Stat. 27.067*** 12.268 Critical Value 15.759 20.161

Lags 15 15 15 Normalized cointegrating equations. Normalized on ltb3m Panel (a) Panel (b) Panel (c)

ltb3m 1.0000 1.0000 1.0000

ltb6m -1.0622*** -1.0658*** 1.0527*** (0.01372) (0.0138) (0.0121)

llbr3m -0.7239*** -0.7171*** (0.0397) (0.0384)

llbr6m 0.7688*** 0.7653*** (0.0405) (0.0398)

Lffr -0.0013 0.0293** (0.0049) (00112)

Constant 0.0419 0.0429 0.1619 (0.0111) (0.0093) (0.0207)

Log likelihood 76,056.44 65,475.04 33,726.65 Estimated system of equations: Xt = µ + Σ Ai Xt - + єt Upper half of the Table 3, Panels (a), (b), and (c) contain hypotheses tests of three cointegrationg equations. In Panels (a) and (b) the inference is two cointegrating vectors and one in Panel (c). Panels (a), (b), and (c) in the lower part of Table 3 contain the normalized cointegrating vectors. Standard errors are in parentheses. ***, ** Significance at 1% and 5% levels.

The magnitude of the error-correction coefficient of Δlffr is much larger, over four times larger, than the coefficient of Δltb3m. Following a positive shock the system is pushed above its equilibrium path. Both lffr and ltb3m react to gradually restore long-run equilibrium relationship between these rates. The negative signs of the error-correction coefficients indicate the direction of that adjustment to equilibrium. Additionally, it appears that the fed funds rate, the focus of monetary policy, reacts significantly in restoring equilibrium. This finding is consistent with the reported results of the studies by Sarno et al (2003) and Zhou (2007). According to Sarno et al (2003) study, ‘the more surprising result was the finding that that the fed funds rate adjusts more rapidly than the three-month T-bill rate’. Likewise, the study by Zhou (2007), that examined the dynamic relationship between the fed funds rate and the Eurodollar rate, reported that, in the post-1994 period the fed fund rate bears the burden of the adjustment toward equilibrium. In general, the belief is that the fed funds rate is the bellwether rate and all other

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short-term rates follow it. That general belief appears to be in contrast with the findings of this study and also the studies by Sarno et al (2003) and Zhou (2007).

Table 4: VECM Estimated Results

Panel (a) Δlffr Δltb3m Δltb6m Δllbr3m Δllbr6m *** Error-correction Coeff. (λi) -0.0867 0.0095*** -0.0038 0.0034 0.0012 (-8.1021) (0.0035) (1.5746) (1.2029) (0.4032) Sums of lagged first differences: ΣΔlffrt-i 0.0544*** 0.0019 -0.0763 -0.0082 (3.9040) (0.8990) (-1.0971) (1.5244) Sums of lagged first differences: ΣΔltb3mt-i 0.0204 -0.0109 0.1996*** 0.2133*** (0.3980) (0.8674) (7.5241) (7.2210) Sums of lagged first differences : ΣΔltb6mt-i 0.0876 0.1031 0.1363 -0.0534 (1.2247) (1.4321) (1.4926) (-0.9327)

Sums of lagged first differences: ΣΔllbr3mt-i 0.1196 -0.1273 0.0326 -0.1358*** (1.6542) (1.4634) (1.2237) (-2.9050) Sums of lagged first differences: ΣΔllbr6mt-i 0.9463 0.0671 -0.0295 0.3068 (1.1223) (1.6454) (1.2895) (1.0722)

Panel (b)

Error-correction Coeff. (λi) -0.1367*** -0.0123*** -0.0048*** -0.0041*** (-16.797) (-4.4635) (-3.5403) (-2.5986) Sums of lagged first differences: ΣΔltb3mt-i -0.1006*** 0.3670 0.0741 (2.6524) (2.6948) (1.0212) Sums of lagged first differences: ΣΔltb6mt-i 0.0625*** -0.0480 -0.0044 (3.5424) (-1.0145) (-0.0875) Sums of lagged first differences : ΣΔllbr3mt-i 0.0329*** -0.0143 -0.0585*** (3.6687) (-2.1580) (-2.5431) Sums of lagged first differences: ΣΔllbr6mt-i 0.0303 -0.0009 0.4552*** (0.9009) (-1.1057) (4.7224) This table shows VECM Estimated Results. III indicates significance at the one percent level.

The short-run dynamics can be inferred from the reported coefficients of the sums of the log differenced and lagged variables in each equation. The reported results in Table 4 panel (a) in the Δltb3m equation, the coefficient of ΣΔlffrt-i is statistically significant while the coefficient of ΣΔltb3mt-i is not significant in the Δlffr equation. The inference is that Granger causality runs from the federal funds rate to the three- month T-bill rate. Next, one-way Granger causation can be inferred in the short-run between three-month T-bill rate to both three-month and six-month Libor, because in the two equations, Δllbr3m and Δllbr6m, the coefficients of ΣΔltb3mt-i, are significant, columns 5 and 6, panel (a). Finally, none of the variables is causally linked to the six-month T-bill rate because none of coefficients in that equation is statistically significant.

Now, let us take a look at the reported results of the error-correction model in panel (b) of Table 4. This model is estimated without the fed funds rate as part of the system. These results are different compared to the reported results in panel (a) of Table 4. First, in panel (a), only the three-month T-bill rate and fed funds rate participate in the adjustment process when the system is out of equilibrium, whereas the results in panel (b) of Table 4 imply that all the four market rates participate significantly in the adjustment process. Secondly, the short-run dynamics among the variables also differ from the results in panel (a) of Table 4. First, in the Δltb3m equation six-month T-bill rate along with the three-month Libor rate cause the three-month T-bill rate. And the three-month T-bill rate causes the three-month Libor. Further, three- month Libor and six-month Libor are mutually causal. In sum, three-month T-bill rate and three-month Libor are mutually causal, three-month T-bill rate and six-month T-bill rate are mutually causal, and the three-month and six-month Libor are mutually causal. These results are not consistent with the results reported in panel (a) of Table 4. The key difference between the two estimated results is the finding of mutual causation among the four rates. The interpretation of the finding of causality running both directions is not very clear. Some of the studies reviewed above argued that a finding of mutual causation of these rates would imply increased integration of the two markets. Results of this study, reported in

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Table 4 panel (b) do indicate mutual causation of these rates. If it is assumed that a finding of mutual causation is evidence of increased integration then, this study supports that contention. Instead of basing the inference of increased market integration on a finding of mutual causation of variables, I would argue that the real support for increased market integration has to be based on the number of ways a system can be stable, that is, the number of cointegrating vectors in the system. More ways a system is be stable, that is, the more cointegrating vectors there are, the more integrated the system is. Dickey et al (1991) argued that cointegrating vectors can be thought of as representing constraints that an economic system imposes on the movement or co-movement of the variables in the system in the long-run. Dickey et al (1991) further assert that the more cointegrating vectors there are the more stable the system. Other things being the same, it is desirable for an economic system to be stationary in as many directions as possible. In this paper the inference of increased integration of the markets is based on the finding of two cointegrating vectors among the five interest rate series.

Further, there is some controversy regarding the interpretation of the finding of mutual causation between variables. Economists Hess and Schweitzer (2000) raised objection to the interpretation of mutual causation as the two variables concerned cause each other. They argued that two variables may ‘Granger- cause one another’, in which case one can conclude only that both economic series are determined simultaneously; hence, a researcher cannot conclude that one series has an independent causal effect on the other’. A second interpretation may be that the two variables involved are caused by a third variable.

The finding of conflicting causal linkages in this study may be due to the omission of the fed funds rate in the estimated results of the VEC model presented in Table 4 panel (b). Perhaps, the fed funds rate is an essential part of the system. However, this issue needs further investigation and will be explored in more detail in future research.

CONCLUDING REMARKS

This empirical study tests whether there is an increase in the degree of integration among the U.S. effective fed funds rate, three and six-month T-bill rates and three and six-month Libor following two key changes. First, in 1990s the Fed dropped the reserve requirement on Eurodollar bank deposits. Second, the Fed switched from borrowed reserve targeting to targeting fed funds rates in conducting monetary policy. To examine this aspect, multivariate cointegrating equations are estimated to test for long-run equilibrium relationship among these rates, and for the short-run dynamics among these rates, two VEC models are estimated. The data used in this study are daily (5-day) series, and the sample period ranges from January 6, 1986 to September 14, 2009. Each of the interest rate series is tested for the degree of integration using ADF and KPSS tests. All the five series in log levels are found to be I(1). The series are stationary in their log first differences. As all the rates are integrated on the same order multivariate JJ cointegration tests are conducted. The tests identified two cointegrating vectors among the U.S. domestic and offshore Libor rates, which implies that the degree of integration among these rates has increased since the 1990s. No published study reported more than one cointegrating relationship between the U.S. domestic rates and the Eurodollar rates. The inference of increased integration between the U.S. domestic and offshore Eurodollar markets is based more on the finding of two cointegration vectors rather than the finding of mutual causation of the interest rates involved. If mutual causation is accepted as proof of increased integration, then, that finding of mutual causation in this study, should reinforce increased integration. One important policy implication of increased integration is that U.S. monetary policy, fed funds targeting, can be expected to be more effective in closely linked markets.

In order to examine the short run dynamics among the key interest rates two VEC models are estimated, one with all the five interest rates and the other without the fed funds rate. The results of the two VEC models are different. This is somewhat intriguing as there is little agreement between the estimated results of the two VEC models. For instance temporal Granger causality runs from the three-month T-bill rate to

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the three-month Libor in the VEC model with the fed funds rate included in the system. The results of the VEC model estimated without the fed funds rate showed bi-directional Granger causality between the three-month T-bill rate and the three-month Libor. These conflicting results will be further investigated as part of further research of this paper.

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ACKNOWLEDGEMENTS

I would like to thank the two anonymous referees and the editor of the journal for their valuable suggestions for the improvement of this paper. Also, I would like to thank Dr. Hal Snarr, my colleague at North Carolina A&T State University, for helping me in making the necessary changes to the manuscript to comply with the journal’s guidelines.

BIOGRAPHY

Krishna M. Kasibhatla is Associate Professor of Economics, Department of Economics and Finance, School of Business and Economics at North Carolina A&T State University (AACSB accredited). His research appeared, either as lead author, sole author, or co-author of the papers published in Empirical Economics, Economic systems, International Journal of Finance, American Economist, International Advances in Economic Research, American Business Review, Indian Journal of Business and Economics, Journal of Economic and Business Studies . He can be reached at 1601 E. Market Street, Greensboro, NC 27411, [email protected]

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Mary Layfield Ledbetter, Nova Southeastern University Carmen Rios, Universidad del Este

John Ledgerwood, Embry-Riddle Aeronautical University Matthew T. Royle, Valdosta State University

Yen-Hsien Lee, Department of Finance, Chung Yuan Tatsiana N. Rybak, Belarusian State Economic University Christian University Rafiu Oyesola Salawu, Obafemi Awolowo University YingChou Lin, Missouri University of Science and Paul Allen Salisbury, York College, City University of Technology New York Shulin Lin, Hsiuping University of Science and Leire San Jose, University of Basque Country Technology Celsa G. Sánchez, CETYS Universidad Melissa Lotter, Tshwane University of Technology I Putu Sugiartha Sanjaya, Atma Jaya Yogyakarta Xin (Robert) Luo, Virginia State University University, Indonesia Andy Lynch, Southern New Hampshire University Sunando Sengupta, Bowie State University Eduardo Macias-Negrete, Instituto Tecnologico de Ciudad Brian W. Sloboda, University of Phoenix Juarez Adriana Patricia Soto Aguilar, Benemerita Universidad Abeer Mahrous, Cairo university Autonoma De Puebla Gladys Marquez-Navarro, Saint Louis University Smita Mayuresh Sovani, Pune University Jesús Apolinar Martínez Puebla, Universidad Autónoma Alexandru Stancu, University of Geneva and IATA De Tamaulipas (International Air Transport Association) Cheryl G. Max, IBM Jiří Strouhal, University of Economics-Prague Aurora Irma Maynez Guaderrama, Universidad Autonoma Qian Sun, Kutztown University de Ciudad Juarez Diah Suryaningrum, Universitas Pembangunan Nasional Romilda Mazzotta, University of Calabria Veteran Jatim Mary Beth McCabe, National University James Tanoos, Saint Mary-of-the-Woods College Avi Messica, Holon Institute of Technology Jeannemarie Thorpe, Southern NH University Cameron Montgomery, Delta State University Ramona Toma, Lucian Blaga University of Sibiu-Romania Sandip Mukherji, Howard University Jorge Torres-Zorrilla, Pontificia Universidad Católica del Tony Mutsue, Iowa Wesleyan College Perú

Cheedradevi Narayanasamy, Graduate School of Business, Md Hamid Uddin, University Of Sharjah National University of Malaysia Ozge Uygur, Rowan University Erwin Eduardo Navarrete Andrade, Universidad Central de K.W. VanVuren, The University of Tennessee – Martin Chile Vijay Vishwakarma, St. Francis Xavier University Dennis Olson, Thompson Rivers University Ya-Fang Wang, Providence University Godwin Onyeaso, Shorter University Richard Zhe Wang, Eastern Illinois University Bilge Kagan Ozdemir, Anadolu University Jon Webber, University of Phoenix Dawn H. Pearcy, Eastern Michigan University Jason West, Griffith University Eloisa Perez, MacEwan University Wannapa Wichitchanya, Burapha University Pina Puntillo, University of Calabria (Italy) Veronda Willis, The University of Texas at San Antonio Rahim Quazi, Prairie View A&M University Bingqing Yin, University of Kansas Anitha Ramachander, New Horizon College of Engineering Junye Yu, Louisiana State University Charles Rambo, University of Nairobi, Kenya

REVIEWERS The IBFR would like to thank the following members of the academic community and industry for their much appreciated contribution as reviewers.

Ana Ma. Guillén Jiménez, Universidad Autónoma de Baja Haydeé Aguilar, Universidad Autónoma de Aguascalientes California

María Antonieta Andrade Vallejo, Instituto Politécnico Ana Ma. Guillén Jiménez, Universidad Autónoma de Baja Nacional California

Olga Lucía Anzola Morales, Universidad Externado de Araceli Gutierrez, Universidad Autonoma De Colombia Aguascalientes

Hector Luis Avila Baray, Instituto Tecnologico De Cd. Andreina Hernandez, Universidad Central de Venezuela Cuauhtemoc Arturo Hernández, Universidad Tecnológica Graciela Ayala Jiménez, Universidad Autónoma de Centroamericana Querétaro Alejandro Hernández Trasobares, Universidad de Zaragoza Carlos Alberto Cano Plata, Universidad De Bogotá Jorge Alma Delia Inda, Universidad Autonoma Del Estado De Tadeo Lozano Baja California Edyamira Cardozo, Universidad Nacional Experimental De Terrance Jalbert, The IBFR Guayana Gaspar Alonso Jiménez Rentería, Instituto Tecnológico de Sheila Nora Katia Carrillo Incháustegui, Universidad Chihuahua Peruana Cayetano Heredia Lourdes Jordán Sales, Universidad de Las Palmas de Gran emma casas medina, Centro de Estudios Superiores del Canaria Estado de Sonora Santiago León Ch., Universidad Marítima del Caribe Benjamín Castillo Osorio, Universidad Cooperativa De Colombia y Universidad De Córdoba Graciela López Méndez, Universidad de Guadalajara- Jalisco Benjamin Castillo Osorio, Universidad del Sinú-Sede Monteria Virginia Guadalupe López Torres, Universidad Autónoma de Baja California María Antonia Cervilla de Olivieri, Universidad Simón Bolívar Angel Machorro Rodríguez, Instituto Tecnológico de Orizaba Cipriano Domigo Coronado García, Universidad Autónoma de Baja California Cruz Elda Macias Teran, Universidad Autónoma de Baja California Semei Leopoldo Coronado Ramírez, Universidad de Guadalajara Aracely Madrid, ITESM, Campus Chihuahua

Esther Eduviges Corral Quintero, Universidad Autónoma Deneb Magaña Medina, Universidad Juárez Autónoma de de Baja California Tabasco

Dorie Cruz Ramirez, Universidad Autonoma Del Estado Carlos Manosalvas, Universidad Estatal Amazónica

De Hidalgo /Esc. Superior De Cd. Sahagún Gladys Yaneth Mariño Becerra, Universidad Pedagogica y Edna Isabel De La Garza Martinez, Universidad Autónoma Tecnológica de Colombia

De Coahuila Omaira Cecilia Martínez Moreno, Universidad Autónoma Javier de León Ledesma, Universidad de Las Palmas de de Baja California-México

Gran Canaria - Campus Universitario de Tafira Jesus Carlos Martinez Ruiz, Universidad Autonoma De Hilario Díaz Guzmán, Universidad Popular Autónoma del Chihuahua

Estado de Puebla Alaitz Mendizabal, Universidad Del País Vasco

Cesar Amador Díaz Pelayo, Universidad de Guadalajara, Alaitz Mendizabal Zubeldia, Universidad del País Vasco/ Centro Universitario Costa Sur Euskal Herriko Unibertsitatea

Elizabeth Avilés , CICESE Fidel Antonio Mendoza Shaw, Universidad Estatal De Ernesto Geovani Figueroa González, Universidad Juárez Sonora del Estado de Durango Juan Nicolás Montoya Monsalve, Universidad Nacional de Ana Karen Fraire, Universidad De Gualdalajara Colombia-Manizales

Teresa García López, Universidad Veracruzana Jennifer Mul Encalada, Universidad Autónoma De Yucatán

Helbert Eli Gazca Santos, Instituto Tecnológico De Mérida Alberto Elías Muñoz Santiago, Fundación Universidad del Norte

Erika Olivas, Universidad Estatal de Sonora Pol Santandreu i Gràcia, Universitat de Barcelona, Santandreu Consultors Erick Orozco, Universidad Simon Bolivar Victor Gustavo Sarasqueta, Universidad Argentina de la José Manuel Osorio Atondo, Centro de Estudios Superiores Empresa UADE del Estado de Sonora Jaime Andrés Sarmiento Espinel, Universidad Militar de Luz Stella Pemberthy Gallo, Universidad del Cauca Nueva Granada Andres Pereyra Chan, Instituto Tecnologico De Merida Jesus Otoniel Sosa Rodriguez, Universidad De Colima Adolfo León Plazas Tenorio, Universidad del Cauca Edith Georgina Surdez Pérez, Universidad Juárez Hector Priego Huertas, Universidad De Colima Autónoma de Tabasco

Juan Carlos Robledo Fernández, Universidad EAFIT- Jesús María Martín Terán Gastélum, Centro de Estudios Medellin/Universidad Tecnologica de Bolivar-Cartagena Superiores del Estado de Sonora

Humberto Rosso, Universidad Mayor de San Andres Jesús María Martín Terán Gastélum, Centro de Estudios

Superiores del Estado de Sonora José Gabriel Ruiz Andrade, Universidad Autónoma de Baja California-México Jesus María Martín Terán Terán Gastélum, Centro de

Estudios Superiores del Estado de Sonora Antonio Salas, Universidad Autonoma De Chihuahua

Claudia Nora Salcido, Facultad de Economía Contaduría y Maria De La Paz Toldos Romero, Tecnologico De Monterrey, Campus Guadalajara Administración Universidad Juarez del Estado de Durango

Abraham Vásquez Cruz, Universidad Veracruzana Juan Manuel San Martín Reyna, Universidad Autónoma de Tamaulipas-México Lorena Vélez García, Universidad Autónoma de Baja

California Francisco Sanches Tomé, Instituto Politécnico da Guarda

Edelmira Sánchez, Universidad Autónoma de Ciudad Alejandro Villafañez Zamudio, Instituto Tecnologico de Matamoros Juárez

Deycy Janeth Sánchez Preciado, Universidad del Cauca Hector Rosendo Villanueva Zamora, Universidad Mesoamericana María Cristina Sánchez Romero, Instituto Tecnológico de Orizaba Oskar Villarreal Larrinaga, Universidad del País Vasco/Euskal Herriko Universitatea María Dolores Sánchez-Fernández, Universidade da Coruña Delimiro Alberto Visbal Cadavid, Universidad del Magdalena

Luis Eduardo Sandoval Garrido, Universidad Militar de Nueva Granada

HOW TO PUBLISH

Submission Instructions

The Journal welcomes submissions for publication consideration. Authors wishing to submit papers for publication consideration should visit our website at www.theibfr.com/journal.htm, under “How to Submit a Paper.” Complete directions for manuscript submission are available at the Journal website www.theIBFR. com/journal.htm. Papers may be submitted for initial review in any format. However, authors should take special care to address spelling and grammar issues prior to submission. Authors of accepted papers are required to precisely format their document according to the guidelines of the journal.

There is no charge for paper reviews. The normal review time for submissions is 90-120 days. However, authors desiring a quicker review may elect to pay an expedited review fee. Authors of accepted papers are required to pay a publication fee based on the length of the manuscript. Please see our website for current publication and expedited review rates.

Authors submitting a manuscript for publication consideration must guarantee that the document contains the original work of the authors, has not been published elsewhere, and is not under publication consideration elsewhere. In addition, submission of a manuscript implies that the author is prepared to pay the publication fee should the manuscript be accepted.

Subscriptions

Individual and library subscriptions to the Journal are available. Please contact us by mail or by email to: [email protected] for updated information.

Contact Information

Mercedes Jalbert, Managing Editor The IBFR P.O. Box 4908 Hilo, HI 96720 [email protected]

Website www.theIBFR.org or www.theIBFR.com

PUBLICATION OPPORTUNITIES Business Education EBA & Accreditation Review of Business & Finance Studies Business Education and Acreditation (BEA) Review of Business & Finance Studies (ISSN: 2150- Business Education & Accreditation publishes high-quality 3338 print and 2156-8081 online) publishes high-quality articles in all areas of business education, curriculum, studies in all areas of business, finance and related educational methods, educational administration, advances fields. Empirical, and theoretical papers as well as case in educational technology and accreditation. Theoretical, studies are welcome. Cases can be based on real-world or empirical and applied manuscripts are welcome for hypothetical situations. publication consideration.

All papers submitted to the Journal are double-blind All papers submitted to the Journal are double-blind reviewed. The Journal is distributed in print and through reviewed. BEA is is listed in Cabell’s and Ulrich’s SSRN and EBSCOhost Publishing, with nation-wide Periodicals Directory. The Journal is distributed in print, access in more than 70 countries. The Journal is listed in through SSRN and EBSCOHost publishing, with presence Cabell’s directory. in over 70 countries.

The journal accept rate is between 15 and 25 percent The journal acceptance rate is between 15 and 25 percent.

A Accounting T & Taxation

Accounting and Taxation (AT)

Accounting and Taxation (AT) publishes high-quality articles in all areas of accounting, auditing, taxation and related areas. Theoretical, empirical and applied manuscripts are welcome for publication consideration. All papers submitted to the Journal are double-blind reviewed. AT is listed in Cabell’s and Ulrich’s Periodicals Directory. The Journal is distributed in print, through SSRN and EBSCOHost publishing, with presence in over 70 countries. The journal acceptance rate is between 5 and 15 percent.