Efficiency of Interbank Money Market: Evidence from Thailand

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Efficiency of Interbank money market: Evidence from Thailand

Gongkhonkwa Rujira (Corresponding Author) Building7 Room104, Huahong Foreign Student Apartment, C3 District, Huazhong University of Science and Technology, Hongshan Area, Wuhan City, Hubei Province, CHINA 430074

Wang Zongjun Room552, School of Management, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan City, Hubei Province, CHINA 430074

Abstract In recent years, many researchers are taking into account the interbank money market which concerning borrow and lend money between banks. This study aims to examine the interbank money market efficiency by using the “Efficient Market Hypothesis” to evaluate how SIBOR and LIBOR effect on BIBOR. To test the market efficiency we apply the ordinary least squares. We found both SIBOR and LIBOR in some tenor have a negative relation on BIBOR. The cointegation test and impulse response analysis suggest SIBOR has more impact on BIBOR than LIBOR in tenor 3months, 6months, and 9months. Conversely, LIBOR has more impact on BIBOR than SIBOR in tenor 1week, 1months, and 2months. The result from this study will be improve the knowledge and understanding about interest rate risk between markets and the central bank or financial institutions can use this result to indicate and to regulate the monetary policy or the bank’s policy. Keywords: Interbank money market; Bangkok Interbank Offered Rate; Singapore Interbank Offered Rate; London Interbank Offered Rate.

1. Introduction In recently globalization era, financial markets enhance growth and continue to grow up therefore many financial institutions are concern about the risk which can transfer between countries and markets. As a result, the market efficiency should be reflecting the information and news that may impact to market situation immediately. Owing to current global financial crisis has an impact on many sector of economy around the world. Owing to the financial market is a significant part of Thailand’s economy which present the stability and soundness of financial market and economic system. Thailand’s Interbank money market is one market that also has a significant effect from financial crisis such market stability therefore many researcher who take into account the global financial crisis they would like to study how global financial crisis effect to financial market stability. Regarding, the interbank money market is some part of Thailand’s financial market which refers to borrowing and lending money between surplus bank and deficit bank. In accordance with Norman E. Cameron (1984), the money market is important for two reasons. First, changes in money market’s yields provide a signal and can be an indicator for the other financial market because the money market is large auctions market reflecting demand and supply from all sectors of economy and its rates serve as important guides to rate setters in other. Second, the market in which monetary policy has its first impact via the central banks. The banks invest excess cash reserves in liquid assets bought in the money market thus money market yield are driven down whenever excess cash reserves are increased. Little real investment is financed in the money market and real assets are not close substitutes for money market assets but the money market rates have an indirect effect on real investment by their effect on other interest rates to which real investment is sensitive. Hence, the tools to assess the market efficiency are measure the variable that may impact to the market situation by adjustment process of price in the market. This study base on the efficient market hypothesis to investigate the

ijcrb.webs.com APRIL 2013 INTERDISCIPLINARY JOURNAL OF CONTEMPORARY RESEARCH IN BUSINESS VOL 4, NO 12 interbank money market efficiency by evaluates the relationship and the degree of pass through between variables.

The rest of this paper is organized as follows, first part is describes the data and review of previous study. Second part is research methodology and hypothesis. Third part is presents basic statistic of the spread of BIBOR over SIBOR and LIBOR, ordinary least squares (OLS) analysis, unit root test, granger causality test, and cointegation test. The last part is the conclusion and suggestion.

2. Description of data and review of literature 2.1 Definition of market and financial market A market is consists of the buyers and sellers that communicate and trade at relatively low cost with one another for voluntary exchange (Ivan and Dale, 2007). The financial market is much more significantly part for moving the economic sector as a finding and management of funds therefore if the financial market does not stability as a result the economics will cannot move on to growing up to be an market efficiency as well (Fama, 1998). Moreover, the economic sector will be fluctuation and decreasing in level of investor confidence index that because market sentiment has an impact on the behavior of investors (Debasish, 2011). In the past few year after the global financial crisis was happened as a result all markets around the world were fluctuated, for instance, the stock market was dropped down dramatically and the financial market was lacked of liquidity and bankruptcy in many banks that owing to the problem of counterparty risk and credit risk. Moreover, the recently financial market has a rapidity changing and connecting to each other which seems likely if one market has a problem that may lead to domino effects both direct and indirect to the other market (Upper and Worms, 2004).

2.2 Definition of efficient market hypothesis: EMH The efficient market hypothesis defined by Eugene Fama in 1970 and later updated in 1991, First efficient market hypothesis assume the financial markets are “informational efficient” which anytime the information can be effect to the prices and fully reflect available information in the market anyway the adjustment process should be almost immediately (Norman E., 1984), such as the stock price should be reflect all information in the market. Next efficient market hypothesis assume the price should come close to reflecting all available information at the end of trading day therefore the adjustment period is a few hours which the directly implication of the efficient market hypothesis is the changes in price during any trading day are owing to the new information. However the prices do not adjust to the old information because if there were, the market professionals or investors would realize and revise their investment strategy to take advantage of it, for instance, they would buy early when adjustment would later cause prices to rise whereas sell early in the opposite situation so their actions would cause prices to rise in immediately. Therefore, the changes of stock prices caused by new information must be random. The efficient market hypothesis is also known as the “random walk hypothesis”. There are three common forms in the efficient market hypothesis that the different forms of market efficiency is depending on the specification of the information (Shleifer, 2000; Stephen et al., 2005). 1) Weak Form or random walk is the current prices will be reflecting the past prices that the movement of future prices cannot be predicted by using the prices from the past or another historical data (Vladimir V., 2008; Wiston, 2008) 2) Semi-strong Form is refer to the price reflects all public information that the prices adjust to publicly available new information very rapidly and in an unbiased. 3) Strong Form is the prices reflect all information, such as public and private therefore nobody can earn excess returns or we called “Perfect Competition Market”. Gregory and Jay (2001) note that “the volatility of private information, a noisy trading and the timing of public information releases those are higher when the market is open”. Moreover, in case of the stock price Jeffrey A. and T. Clifton (2002) found the Midday Call segment on the cable television financial news had a positive significant on the stock prices and Noh-Sun (2002) also found “ the stock prices are adjust to new information faster than real

ijcrb.webs.com APRIL 2013 INTERDISCIPLINARY JOURNAL OF CONTEMPORARY RESEARCH IN BUSINESS VOL 4, NO 12 variables”. However, if no useful information in market price the prices can be understood as noise (Joseph et al., 2008). In contrast, Jeffrey A and T. Clifton (2002) suggest that “in practice the prices do not respond instantaneously to the news”. However, Allan and Clive (2004) suggest more detail definition of market efficiency as “the market is efficient with respect to the information set” “with the high frequency data not with the medium or low frequency data” A.S.M. Sohel (2009). Vladimir (2008) mention that “in case if the market is efficient in the weak form the behavior of prices in markets on traded of stock, bonds, or property in present will reflects all available information therefore the past price cannot be useful for abnormal profit”. In addition, the efficient market hypothesis by John and Allan D. (1997), they focused on testing information efficiency in exchange rates and they found the exchange rates are fully and instantaneously reflect all relevant and available information therefore a speculator or banks cannot forecast the future behavior of prices and changes in exchange rate. Conversely, Amelie et al. (2012) suggest that “the return predictability of foreign exchange rates depending on changing market conditions” Consequently, the arrival of news or the available information and the processing of information are important determinants of market efficiency and price volatility which the quote adjustments, noise and information volatility should adjust to all relevant variables, economic news, and market condition very quickly.

2.3 Definition of Bangkok Interbank Offered Rate: BIBOR The Bangkok Interbank Offered Rate (BIBOR) is refers to the interbank rate of Thailand’s money market which the banks borrow funds from the other banks in Bangkok interbank market. BIBOR is determined from information that is available during the period of 10.45-11.00 AM (Bangkok time) in each working day. The average of the borrowing rates quoted by predetermined banks which determined from rates quoted by 17 contributors (www.bot.or.th), the average rate is derived by eliminating the first two highest and lowest of the quotes and arithmetic-averaging the remaining rates for the day. The BIBOR is a reference rate which is fixed at 11.00 a.m. of each working day and published by BOT at 11.15 a.m. in every working day. The central bank in every country usually use the average overnight interbank market rate as an indicator for expected the liquidity situation on the interbank market (Jens, 2006). However, the overnight rate is depending on the monetary policy moreover Dieter and Jan (2011) suggest that “the dynamics of interest rates perhaps depending on the frequency of open market operation”. As some evidence of Asimakis and Nicholas (2006) note that “the changing in Bank of Greece’s official influence on short-term and intermediate-term market interest rate. Some of previous literature which relevant to the interest rate include, Nikola and Ramazan (2008), they mentioned that “entropy performs reflected in interest rates and the behavior of short-term interest rates appears to reflect cross-sectional and time-series”. Owing to, the crisis is an unpredictable event, as a result an uncertainty about future interest rates can be substantially affected by how the central bank conducts its monetary policy and forecast future interest rate (Martin, 2012) at the same time the central bank have to protect against economic turmoil and set up the decision making process to ensure a good governance and transparency in the economic changes for each period.

2.4 Definition of Singapore Interbank Offered Rate: SIBOR The Singapore Interbank Offered Rate (SIBOR) is refers to the interbank rate of Singapore’s money market for borrow and lend between the financial institutions in Singapore. Moreover, SIBOR is directly relevant to Thailand’ financial market in case of using for calculates the Thai Baht Interest Rate Fixing (THBFIX) which we will use the SIBOR, the average USD/THB forward exchange rate, and average USD/THB spot exchange rate for calculate the THBFIX. During the period of global financial crisis the financial institutions are concern with the counterparty risk, default risk and the other risk that may affect to the banks liquidity. As a result, the banks will feel concern when they lend their money to the banks who deficit the money. In case, if the surplus bank feel fear on counterparty to lend their money as a result they will decrease lending transactions or using some special condition of the bank’s policy then the cost of lending should be higher because of the lender banks want to protect all the risks that will

ijcrb.webs.com APRIL 2013 INTERDISCIPLINARY JOURNAL OF CONTEMPORARY RESEARCH IN BUSINESS VOL 4, NO 12 happen in the future or after they done the transactions with counterparty until the maturity date of transactions. Therefore, the liquidity between financial institutions is able to be “Credit Crunch” or the liquidity between banks will be decreased. Some of previous literature that relevant to our study includes, Ramin and Tiong (2000) suggest that “short-and long-term interest rates have a significant effect on the changing in Singapore’s stock market but the stock prices are negatively related with the Singapore dollar exchange rates (Ying, 2001). In addition, Giorgio (2009) mentioned that “domestic interest rates react to both external and domestic monetary policy announcements”.

2.5 Definition of London Interbank Offered Rate: LIBOR The London Interbank Offered Rate (LIBOR) is applied to credit operations between banking institutions. On the Euro money market the LIBOR is a rate of interest that major international banks in London charge each other for loans of Eurodollars overnight (Stephen, 2005), and it is a primary benchmark for short-term interest rate which used as the basis for settlement of interest rate contracts on many of the world’s major futures and options exchanges as well as most Over-the-Counter (OTC) and lending transactions. And in the US are based on spreads off LIBOR by contrast with the bid rate LIBID quoted by banks seeking such deposits. The LIBOR is fixed on a daily basis by the British Bankers' Association which calculated daily and serves as a guide for the interest rates which calculated for 10 currencies include, Australian dollar, Canadian dollar, Danish krone, Euro, Japanese yen, New Zealand dollar, Pound sterling, Swedish krona, Swiss franc, and US dollar (http://www.bba.org.uk). The British Bankers’ Association (BBA) is the leading trade association for the UK banking and financial services sector that the BBA collectively provide the full range of banking, financial services, and to make up the world’s largest international banking centre (http://www.bba.org.uk). As some evidence of the previous literature that relevant to our study, Philip and Francis (2010) note that “the LIBOR spread had quickly widened and to be able to become volatility during the period of crisis. Moreover, in case if compare the LIBOR with other short-term borrowing rate as a result the actual LIBOR does not different from the predicted values (Rosa et al., 2012). Additionally, Lee and Timothy (1999) suggest that “the volatility of 3-month and 6-month London Interbank Offer Rates are proportional to the level of the respective rate with the factor of proportionality less than one”. As Ramaprasad et al. (2006) suggest that “the dynamics for LIBOR rate may be characterized by higher interest rate elasticity”.

3. Research methodology and hypothesis This study intently to assess and explain an efficiency of information on market follows the efficient market hypothesis (Fama, 1970). As mentioned before, the Bank of Thailand uses the BIBOR to reference on interbank money market. Therefore, in this part we will apply the efficient market hypotheses to be our main assumption thereby our independent variables include, SIBOR, and LIBOR for testing the information efficiency on BIBOR. The source of time series data is from the Bank of Thailand that covers the sample period from January 2006 to December 2011. Hence, to analyze the relationship between each variable for testing the pass through of information from one market into the other market or the information efficiency we using the econometric test by ordinary least squares (OLS) with multiple linear regression to be our model owing to our data is a time series data that concerning the historical rate of BIBOR, SIBOR, and LIBOR and our study aim to investigate the relationship between each variable by linear trend therefore the suitable methodology for testing should be a econometric test by ordinary least squares analysis. According to analysis the relationship between variables by OLS, before testing the OLS we must test a unit root owing to the time series data can be non-stationary and will make a spurious relationship between variables. Afterward, we will analyze the cointegation test and unit root test, the impulse response function test, and the granger causality test. Consequently, the null hypothesis of our study is H0: the relationship between BIBOR and SIBOR, LIBOR should be relevant to each other because of the information in one market should pass through into the other market following the efficiency market hypothesis.

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4. Results 4.1 The spreads of BIBOR over SIBOR and LIBOR Before analyze the OLS, in this part we firstly describe the basic statistic such the spread of BIBOR over the SIBOR and LIBOR in all tenors during 2006 to 2011. Comparing the basic statistics of the spread of BIBOR over SIBOR and the spread of BIBOR over LIBOR in table1, we found the mean, median, and maximum in the spread of BIBOR over LIBOR were more than the spread of BIBOR over SIBOR almost every tenor even though the minimum of the spread of BIBOR over LIBOR also almost higher too. The standard deviation suggests that the spread of BIBOR over LIBOR is a litter small than the spread of BIBOR over SIBOR that indicate the spread of BIBOR over LIBOR is less volatile than the spread of BIBOR over SIBOR. The spread of BIBOR over SIBOR has negative skewness in tenor 1week, 1month, and 2months which suggest that the distribution is skewed left whereas in tenor 3months, 6 months, 9months, and 12months are skewed right, however every tenor is distribution close to normal distribution. The skewness in the spread of BIBOR over LIBOR is skewed left in tenor 1week and 1month whereas the other tenor is skewed right and all tenor also distribution nearly normal distribution as well. Last basis statistic, the kurtosis that suggest the spread of BIBOR over SIBOR and LIBOR in tenor 12months are more peaked than the other tenor.

[Insert Table1 here]

4.2 Ordinary least squares (OLS) analysis As mentioned in previous section, we specific analyze the relationship of BIBOR, SIBOR, and LIBOR by the ordinary lease squares which apply the efficient market hypothesis (Fama, 1970) to test the information efficiency pass through between markets, moreover in this part we endeavor study the degree of pass through in each variable. Therefore, we perform our tests with ordinary least square (OLS) analysis (Engle and Granger) by using the econometric model in case of the independent is more than one that is multiple linear regressions (MLR). As follow as the multiple linear regression model;

Then we apply the above equation into our study, following multiple linear regression models is estimated with Ordinary Least Square (OLS);

where β0, β1, and β2, are a parameters, BIBORt is Bangkok Interbank Offered Rate (BIBOR), INTsibor is Singapore Interbank Offered Rate (SIBOR) , INTlibor is London Interbank Offered Rate (LIBOR), and t is an Error term.

4.2.1 Unit Root Test According to the time series may able to be non-stationary as a result after we get a statistic test the result will be spurious regression and unbelief therefore before we test the econometric test we have to test the unit root for finding the stationary level. This subsection we will test the unit root with Augmented Dickey-Fuller (ADF). The unit root test with Augmented Dickey–Fuller (ADF) results are show in table 2. For the 1st difference, the null hypothesis is rejected that because of the t-statistic in all tenors of BIBOR, SIBOR, and LIBOR are less than

ijcrb.webs.com APRIL 2013 INTERDISCIPLINARY JOURNAL OF CONTEMPORARY RESEARCH IN BUSINESS VOL 4, NO 12 the MacKinnon Critical value. Consequently, the unit root test with ADF in all tenors of BIBOR, SIBOR, and LIBOR are stationary at 1st difference with the significant level at 1%, 5%, and 10%.

[Insert Table2 here]

4.2.2 Cointegation test and Unit root test (Residual) This subsection we continually study the cointegration test to analyze the direction of the relationship between variables follow our hypothesis which we assume the BIBOR should be relevant to SIBOR and LIBOR. From table3, the coefficient suggests that the direction of the relationship between dependent and independent variables. As mentioned before, we assume the variable according to the multiple linear regression models which the BIBOR is dependent variable and SIBOR and LIBOR are independent variables. The cointegation test and unit root test results are as follows: 1. Tenor 1week: both of SIBOR and LIBOR have a positive relation on BIBOR which LIBOR (0.08106) has an impact on BIBOR more than SIBOR (0.00201). 2. Tenor 1month: SIBOR has a negative relation on BIBOR around -0.00201 whereas the LIBOR has a positive relation on BIBOR around 0.0047. 3. Tenor 2months: SIBOR (-0.12098) has a negative relation on BIBOR whereas LIBOR has a positive relation on BIBOR around 0.19034. 4. Tenor 3months: SIBOR (0.00825) has an impact on BIBOR more than LIBOR (0.00145) however both rates have a positive relation on BIBOR. 5. Tenor 6months: SIBOR has a negative relation on BIBOR around -0.33508 but LIBOR has a positive relation on BIBOR around 0.219. 6. Tenor 9months: both of SIBOR and LIBOR have a negative relation on BIBOR around -0.03651 and -0.00114, respectively. 7. Tenor 12months: both of SIBOR and LIBOR have a negative relation on BIBOR around -0.00306 in SIBOR and -0.00423 in LIBOR. Consequently, SIBOR has a positive relation on BIBOR in tenor 1week and 3months, LIBOR has a positive relation on BIBOR in tenor 1week, 1month, 2months, 3months, and 6 months. Comparing the coefficient value in case we ignore the positive or negative relationship, we will found SIBOR has an effect to BIBOR more than LIBOR in tenor 3months, 6months, and 9months. On the other hand LIBOR has an effect to BIBOR more than SIBOR in tenor 1week, 1months, 2months, and 12months. In addition, from the unit root test (residual) which suggests the long-run relationship between variables, we found both of SIBOR and LIBOR have a long-run relationship on BIBOR in all tenor that owing to the ADF statistic is more than MacKinnon Critical value. [Insert Table3 here]

4.2.3 Impulse Response Function Analysis This subsection we investigate the dynamic time path which suggests the current and future changes of dependent variable when the independent variable shock. As a result, we can explain the direction of relationship both of short-run and long-run relations on BIBOR, SIBOR, and LIBOR. Figure1 present an impact of SIBOR shock and LIBOR shock on BIBOR. Furthermore, we will present the impact of SIBOR shock on LIBOR and LIBOR shock on SIBOR as well. The results from impulse response function analysis are as follows: 1. Tenor 1week: an impact of the SIBOR shock and LIBOR shock on BIBOR were increased from the beginning till the long-run however we will see the LIBOR shock has more impact on BIBOR than SIBOR shock. The response of SIBOR to LIBOR shock was increased dramatically at the beginning but it was little dropped for

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long-run. The response of LIBOR to SIBOR shock was little increased from the beginning till the long-run. 2. Tenor 1month: an impact of the SIBOR shock on BIBOR was increased in short-run but for long-run was decreased whereas the LIBOR shock on BIBOR was dropped in short-run on the other hand for long-run was increased. The response of SIBOR to LIBOR shock was negative in short-run afterward it was positive in long- run. The response of LIBOR to SIBOR shock was increased at the beginning but for a long-run it was decreased. 3. Tenor 2months: an impact of the SIBOR shock and LIBOR shock on BIBOR and the response of LIBOR to SIBOR shock seem like in case of tenor 1month but for the response of SIBOR to LIBOR shock was not negative both in short-run and long-run. 4. Tenor 3months: an impact of the SIBOR shock on BIBOR seems like the previous case but for the LIBOR shock on BIBOR the degree of change was a little bit and was negative in long-run. The response of SIBOR to LIBOR shock was increase at the begging but for the long-run was dropped. The response of LIBOR to SIBOR shock seems like the previous case. 5. Tenor 6months: an impact of the SIBOR shock and LIBOR shock on BIBOR, the response of SIBOR to LIBOR shock, and the response of LIBOR to SIBOR shock almost seem like the case of tenor 2months however the degree of change in this case was little less than the tenor 2months’s case. 6. Tenor 9months: an impact of the SIBOR shock and LIBOR shock on BIBOR seem like in case of tenor 2months and 6months especially the SIBOR shock on BIBOR but the degree of LIBOR shock on BIBOR was less than both of two cases. The degree of change in case response of SIBOR to LIBOR shock was less than both short-run and long-run. And the response of LIBOR to SIBOR shock was increase in short-run and for long-run was little decreased. 7. Tenor 12months: an impact of the SIBOR shock on BIBOR was not different from the previous case and the impact of the LIBOR shock on BIBOR seems like the tenor 9months’s case but the degree of change was little than that case. The response of SIBOR to LIBOR shock seems like the previous case and the response of LIBOR to SIBOR shock almost seems like the tenor 9months’s case. Consequently, in case if we compared the degree of change of the SIBOR shock and LIBOR shock on BIBOR we will found the SIBOR shock has more significant impact on BIBOR than LIBOR shock in tenor 3months, 6months, 9months and the LIBOR shock has more significant impact on BIBOR than SIBOR shock in tenor 1week, 1month, 2months that relevant to the cointegation test. Even though, in tenor 12 months this test has difference result. The impact of the SIBOR shock on BIBOR in every tenor was increased in a short-run but in a long-run was slow down little bit. The impact of the LIBOR shock on BIBOR in tenor 1week, 1month, 2months, 6months, 9months, and 12months were increased in a short-run till a long-run on the other hand in tenor 3months was decreased in both of short-run and long-run. Additionally, the response of SIBOR to LIBOR shock in tenor 1month suggests it was negative in short-run whereas it was positive in long-run. Anyway the response of LIBOR to SIBOR shock was increased at the beginning but little decreased in long-run in almost every tenor.

[Insert Figure1 here]

4.2.4 Granger Causality Test Regarding the previous subsection, the cointegation test and impulse response function analysis those explain us only the direction of the relationship of BIBOR, SIBOR, and LIBOR not explain the causality of variable. Therefore, this subsection testing the causality of variable that consists of the BIBOR, SIBOR, and LIBOR that covers the sample period from January 2006 to December 2011. This testing tries to describe the causes and effects of variable by granger causality testing (1969). We will analyze the causality between variables include, BIBOR and SIBOR, BIBOR and LIBOR in every tenor. Therefore, our hypotheses for testing granger causality are as follows:

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H0: The Singapore Interbank Offered Rate (SIBOR) and London Interbank Offered Rate (LIBOR) cannot cause the Bangkok Interbank Offered Rate.

H1: The Singapore Interbank Offered Rate (SIBOR) and London Interbank Offered Rate (LIBOR) can cause the Bangkok Interbank Offered Rate changes. Table3 reports granger causality testing of BIBOR, SIBOR, and LIBOR in each tenor. The results are as follow: 1. The SIBOR cannot cause the BIBOR in every tenor whereas the BIBOR can cause the SIBOR in tenor 1 week, 3 months, 6 months, 9 months, and 12 months especially in tenor 1 week. 2. The LIBOR cannot cause the BIBOR in every tenor but the BIBOR can cause the LIBOR in tenor 3 months until tenor 12 months particularly in tenor 9 months 6months, and 12 months, respectively.

[Insert Table4 here]

5. Conclusion and suggestion In this study, we endeavor study an information efficiency between market, a degree of pass through, and the relationship between each available by base on the efficient market hypothesis (Fama, 1970). We use the BIBOR, SIBOR, and LIBOR to be our variable and perform our test with ordinary least square by applied the model of multiple linear regressions into our study. From the cointegation test and unit root test (residual), we found some tenor of SIBOR and LIBOR have a negative relation on BIBOR. And we also found SIBOR has more impact on BIBOR than LIBOR in tenor 3months, 6months, and 9months. Conversely, LIBOR has more impact on BIBOR than SIBOR in tenor 1week, 1months, 2months, and 12months. Nevertheless, both of SIBOR and LIBOR have a long-run relationship on BIBOR in all tenor. As the impulse response’ result that suggest and support the finding of cointegation test which the SIBOR shock has a significant impact on BIBOR than the LIBOR shock in tenor 3months, 6months, and 9months. In contrast, the LIBOR shock has a significant impact on BIBOR than the SIBOR shock in tenor 1week, 1month, and 2months. However the short-run and long-run relation on BIBOR in case of the SIBOR shock was increased at the beginning and slows down in a long-run. In case of the LIBOR shock affect to BIBOR seems like the SIBOR shock in every tenor except in tenor 3months that it was decreased from the beginning till long-run. From the granger causality test, we found both of SIBOR and LIBOR cannot cause the BIBOR in all tenors whereas the BIBOR can cause the SIBOR in tenor 1 week, 3 months, 9 months, and especially in tenor 1week anyway the BIBOR can cause the LIBOR in tenor 3 months till tenor 12 months especially in tenor 9 months. However, the SIBOR and LIBOR may not directly causality of BIBOR changes during the sample period but those interest rates have a significant relationship to each other. For the future study, should be studied in the other interbank interest rate, such as TIBOR, MIBOR, or KLIBOR for comparing the degree of pass through in interbank money market for testing the market efficiency.

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Upper, C., Worms, A. (2004). Estimating bilateral exposures in the German interbank market: Is there a danger of contagion?. European Economic Review, 48, 827-849. Vladimir, V. (2008). Market efficiency and the phase-lagging model of the price evolution. Physica A, 387, 861-875. Wiston, A. (2008). The informational efficiency and the financial crashes. Research in International Business and Finance, 22, 396-408. Ying, W. (2001). Exchange rate, stock price, and money markets: evidence from Singapore. Journal of Asian Economics, Vol. 12 No. 3, pp.445-458. Ivan, P. & Dale, L. (2007). Managerial Economics. (3rd ed.). New York: Blackwell Publishing Ltd. Norman, E. (1984). Money financial markets and economic activity. New York: Addison Wesley Publishing Company. Shleifer, A. (2000) Inefficient Markets: An Introduction to Behavioral Finance. United Kingdom: Oxford University Press, Stephen, A., Randolph, W., & Jeffrey, F. (2005). Corporate Finance. (7th ed.). New York: McGraw-Hill Companies Inc. The Bank of Thailand, Interbank money market and Bangkok Interbank Offered Rate. [Online] Available: http://www2.bot.or.th/FinMarkets/Bibor/bibor_th.asp (August 8, 2012) British Bankers’ Association, London Interbank Offered Rate. [Online] Available: http://www.bba.org.uk (August 8, 2012)

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Figures Response to Cholesky One S.D. Innovations ± 2 S.E.

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Figure 1: Impulse responses of BIBOR to SIBOR and LIBOR, SIBOR to LIBOR, and LIBOR to SIBOR in every tenor.

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Tables Table1: The spreads of BIBOR over SIBOR and LIBOR

SIBOR 1w SIBOR 1m SIBOR 2m SIBOR 3m SIBOR 6m SIBOR 9 SIBOR 12m

Mean 0.761 0.772 0.726 0.705 0.638 0.606 0.570

Median 0.990 1.000 0.890 0.800 0.580 0.505 0.455

Maximum 3.310 3.280 3.260 3.240 3.210 3.100 3.000

Minimum -2.090 -2.020 -1.990 -1.960 -1.750 -1.680 -1.710

Std. Dev. 1.367 1.325 1.304 1.299 1.246 1.180 1.132

Skewness -0.028 -0.056 -0.002 0.039 0.190 0.251 0.263

Kurtosis 2.475 2.573 2.670 2.676 2.743 2.857 2.927

LIBOR 1w LIBOR 1m LIBOR 2m LIBOR 3m LIBOR 6m LIBOR 9m LIBOR 12m

Mean 0.764 0.779 0.733 0.711 0.646 0.612 0.583

Median 0.995 0.985 0.890 0.810 0.590 0.515 0.495

Maximum 3.320 3.290 3.270 3.250 3.210 3.100 3.010

Minimum -2.110 -2.000 -1.970 -1.940 -1.740 -1.670 -1.710

Std. Dev. 1.366 1.319 1.302 1.297 1.246 1.181 1.135

Skewness -0.033 -0.052 0.002 0.042 0.192 0.250 0.254

Kurtosis 2.479 2.574 2.671 2.680 2.736 2.845 2.892

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Table 2: Unit root test (ADF-Trend and intercept)

Variable Level Lag 1 Equation test t-statistic Mackinnon Critical Value

BIBOR 1w -3.409681 -3.165046***

BIBOR 1m -3.410955 -3.165046***

BIBOR 2m -3.505466 -3.475305**

BIBOR 3m -3.494804 -3.475305**

BIBOR 6m -3.432156 -3.165046***

BIBOR 9m -3.444522 -3.165046***

BIBOR 12m -3.495475 -3.475305**

SIBOR 1w -12.4198 -4.09455*

SIBOR 1m -7.181071 -4.09455*

SIBOR 2m -6.697044 -4.09455* With trend and SIBOR 3m 1st difference 0 -7.098986 -4.09455* intercept SIBOR 6m -5.896842 -4.09455*

SIBOR 9m -6.68997 -4.09455*

SIBOR 12m -5.787807 -4.09455*

LIBOR 1w -6.423703 -4.09455*

LIBOR 1m -7.393435 -4.09455*

LIBOR 2m -6.897374 -4.09455*

LIBOR 3m -7.124504 -4.09455*

LIBOR 6m -5.997933 -4.09455*

LIBOR 9m -13.53365 -4.09455*

LIBOR 12m -6.604173 -4.09455*

Note: Lag1 = Length (Automatic based on SIC); * = Significant level at 0.01; ** = Significant level at 0.05; *** = Significant level at 0.10

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Table 3: Cointegation test and Unit root test (Residual)

Dependent Independent MacKinnon Coefficient t-statistic Prob. ADF-statistic Result Variable Variable Critical

SIBOR 1w 0.00201 0.22518 0.82270

BIBOR 1w LIBOR 1w 0.08106 1.22227 0.22720 -7.2371 -4.0946* Stationary

Constant -0.02588 -0.95590 0.34360

SIBOR 1m -0.00201 -0.01024 0.99190

BIBOR 1m LIBOR 1m 0.00470 0.02464 0.98040 8.7117 -4.0946* Stationary

Constant -0.00165 -0.19211 0.84840

SIBOR 2m -0.12098 -0.77415 0.44240

BIBOR 2m LIBOR 2m 0.19034 1.12830 0.26450 -5.1343 -4.0946* Stationary

Constant 0.00632 1.33135 0.18900

SIBOR 3m 0.00852 0.05965 0.95270

BIBOR 3m LIBOR 3m 0.00145 0.00903 0.99280 -6.356 -4.0946* Stationary

Constant -0.00870 -1.65364 0.10430

SIBOR 6m -0.33508 -2.19548 0.03270

BIBOR 6m LIBOR 6m 0.21900 1.38798 0.17120 -4.6292 -4.0946* Stationary

Constant 0.00930 1.43952 0.15610

SIBOR 9m -0.03651 -1.25937 0.21360

BIBOR 9m LIBOR 9m -0.00114 -0.53359 0.59590 -6.9325 -4.0946* Stationary

Constant -0.01129 -1.63189 0.10890

SIBOR 12m -0.00306 -0.05822 0.95380

BIBOR 12m LIBOR 12m -0.00423 -0.14335 0.88660 -5.6382 -4.0946* Stationary

Constant 0.01465 1.24326 0.21950

Note: *=Significant level at 0.01

Table4: Granger causality test

Probability (p-values) Null Hypothesis: 1 week 1 month 2 months 3 months 6 months 9 months 12 months

SIBOR does not Granger Cause BIBOR 0.97930 0.68870 0.36508 0.27184 0.16323 0.23174 0.27178

BIBOR does not Granger Cause SIBOR 0.04060* 0.49439 0.28060 0.15771* 0.07408* 0.10559* 0.11969

LIBOR does not Granger Cause BIBOR 0.97455 0.68143 0.37189 0.26920 0.16264 0.21151 0.25382

BIBOR does not Granger Cause LIBOR 0.65902 0.47162 0.27408 0.15716* 0.07091* 0.00883* 0.09519*

Note: * = reject null hypothesis at significant level 0.10