Modelling Volatility Spillover Effects Between Developed Stock Markets and Asian Emerging Stock Markets

Econometrics Working Paper EWP1301 ISSN 1485-6441 Department of Economics Modelling Volatility Spillover Effects Between Developed Stock Markets and Asian Emerging Stock Markets Yanan Li & David E. Giles Department of Economics, University of Victoria September 2013 Abstract This paper examines the linkages of stock markets across the U.S., Japan and six Asian developing countries: China, India, Indonesia, Malaysia, the Philippines and Thailand over the period January 1, 1993 to December 31, 2012. The volatility spillover is modeled through an asymmetric multivariate GARCH model. We find significant unidirectional shock and volatility spillovers from the U.S. market to both the Japanese and the Asian emerging markets. It is also found that the volatility spillovers between the U.S. market and the Asian markets are stronger and bidirectional during the Asian financial crisis. Further, during the last five years, the linkages between the Japanese market and the Asian emerging markets became more apparent. Our paper contributes to the literature by examining both the long run and the short run periods and focusing on shock and volatility spillovers rather than return spillovers, which have been the primary focus of most other studies. Keywords: Volatility, Spillovers, Stock markets, Multivariate GARCH, Asymmetric BEKK model JEL Classifications: C32, C58, G1 Author Contact: David E. Giles, Dept. of Economics, University of Victoria, P.O. Box 1700, STN CSC, Victoria, B.C., Canada, V8W 2Y2; e-mail: [email protected]; Phone: (250) 721-8540; FAX: 1. Introduction The increasing economic integration of international stock markets has become especially important over the last two decades. The substantial development of technology and the increased flow of capital between countries are among the main factors contributing to this observed globalization. So, understanding the nature and extent of linkages between different financial markets is important for portfolio managers and financial institutions. The volatility of returns is often used as a crude measure of the risk of holding financial assets (e.g., Brooks, 2002), so, when referring to international equity markets integration, researchers not only investigate returns causality linkages, but they also measure volatility spillover effects. Information about volatility spillover effects is useful for the application of value at risk and hedging strategies. Recently, with the role of emerging markets becoming more important, economists have focused not only on developed countries (e.g., Theodossiou et al., 1993; Bae et al., 1994; Karolyi 1995), but they have also paid attention to emerging markets (e.g., Worthington et al., 2004; Wang et al., 2004; Goetzmann et al., 2005; Lin et al., 2006; Ng, 2010). For instance, the extent of the linkages between emerging stock markets and developed stock markets has implications for investors in both developing and developed countries. If emerging financial markets are only weakly integrated with their developed counterparts, external shocks may have limited influence on the emerging markets, and then the developed market investors can benefit by including the emerging market stocks in their portfolio, as this diversification should reduce risk. On the contrary, if the emerging stock markets are fully integrated with the developed stock markets, the volatility in the emerging markets may decrease as it will be mainly determined by the developed markets’ volatilities, and the domestic emerging investors will benefit from a low cost of capital (Li, 2007). Thanks to recent developments in econometrics, and the associated software econometrics software1, in addition to examining returns spillovers between equity markets, we can now also estimate volatility spillovers between different markets by using multivariate GARCH (MGARCH) models. We take advantage of these developments, by examining linkages between some emerging Asian stock markets and two developed stock markets. The six emerging Asian markets used in this study are China, India, Indonesia, Malaysia, the Philippines and Thailand2. We use the U.S. and Japan to represent developed 1 Currently, several packages, such as RATS and SAS, allow us to estimate different types of MGARCH models. 2 The IMF also includes Pakistan as an emerging market in Asia, but some other major financial institutes do not include Pakistan as an emerging market. Thus, we do not include Pakistan in our sample. 1 countries in two different geographical regions. We employ an asymmetric BEKK3 model proposed by Engle and Kroner (1995), and improved by Kroner and Ng (1998), to examine both shock and volatility spillovers among each emerging market and the two developed markets. Our data sample spans 20 years, and includes both the 1997 Asian financial crisis and the 2007 subprime financial crisis. Although the empirical finance literature is rich in studies focusing on the transmissions and dynamic linkages between major stock markets, our study distinguishes itself from these in three major respects. First, instead of analyzing transmissions only between developed stock markets, we examine the linkages between the developed markets and Asian emerging markets. Second, we not only examine the long run relationship between different markets, but also compare the results with different sample periods, and the two short-run periods are chosen based on two recent financial crises. Third, and importantly, we consider past shock and volatility spillovers across different markets, whereas the previous literature focuses on returns transmissions. The remainder of this paper is organized as follows. The next section introduces the background of markets’ indices and provides some preliminary statistical analysis of our data. Section 3 outlines the methodology used to analyze the volatility spillover effects, and the empirical results are discussed in section 4. Finally, section 5 summarizes and makes concluding remarks. 2. Data Analysis We used widely accepted benchmark indices for the eight selected countries, with each index describing the overall performance of large-capitalization firms in the respective country, taken to represent the overall equity market in that country. The six Asian emerging stock market indices that we consider are: Shanghai Stock Exchange composite (CHSCOMP) for China; S&P BSE 30 SENSITIVE (IBOMSEN) for India; IDX composite (JAKCOMP) for Indonesia; FTSE BURSA MALAYSIA KLCI (FBMKLCI) for Malaysia; PSE composite (PSECOMP) for the Philippines; and BANGKOK SET 50 (BNGKS50) for Thailand. The two developed market indices are the S&P 500 composite (S&PCOMP) for the U.S.; and TOPIX (TOKYOSE) for Japan. The daily data are obtained from DATASTREAM4. 3 The acronym comes from the name of the original collaborators, Baba, Engle, Kraft and Kroner. This is explained by Engle and Kroner (1995, footnote 1). 4 The DATASTREAM code for each index is given in parentheses. 2 The full sample period under study is from January 1, 1993, to December 31, 2012, except for Thailand5 due to data limitations. The reason for selecting this period is that both the Asian financial crisis and the subprime financial crisis are covered, so in addition to investigating the volatility performance over the long run, we can also explore dynamic linkages across markets over the short run, or over an economic recovery period. Figure 1: Trading Hours for the U.S. and Asian Stock Markets Country Local trading hours Country Local trading hours U.S. 9:30‐16:00 India 9:00‐15:00 Japan 9:00‐15:00 Malaysia 9:00‐17:00 China 9:30‐15:00 The Philippines 9:30‐15:30 Indonesia 9:30‐16:00 Thailand 9:30‐16:30 t t+1 U.S. at t‐1 Asian at t U.S. at t Asian at t+1 18:00 0:00 6:00 12:00 18:00 24:00 6:00 GMT There are 5217 observations in our sample. By using daily data, we can capture more information than with weekly or monthly data. In this study, we set prices on non-trading days to be the same as on the previous trading day.6 Also, our markets are located in different time zones, resulting in different opening and closing times, as is shown in Figure 1 for two consecutive days. Figure A-1 in the Appendix shows the adjusted closing prices of our eight indices. Each index has a trough during 1998 when the Asian financial crisis happened. The troughs in Japan, Indonesia, Malaysia, the Philippines and Thailand are more obvious than those in the U.S., China, and India. However, when the U.S. subprime financial crisis occurred in 2007, each index has a clear trough. Visually, the markets’ integrations are more apparent over the last decade, than during the 1990’s. 5 Thailand’s index is from January 1, 1996, to December 31, 2012. 6 We found that excluding the implied zero returns for certain days did not yield significantly different results. 3 The continuously compounding daily returns for each stock market are expressed in percentages, computed by multiplying the first difference of the logarithm of the market closing value of the index by 100. Figure A-2 in the Appendix shows the returns series for each index. These series exhibit the typical ‘volatility clustering’ of high frequency financial data. Table 1 presents the descriptive statistics of the stock price returns for the eight indices over the full sample period. Table 1: Summary statistics for returns series - Full sample Country USA JPN CHN IDN IND MYS PHL THA Mean 0.0227 -0.0080 0.0205 0.0528 0.0384 0.0185 0.0294 -0.0012 Median 0.0256 0 0 0.0122 0 0 0 0 Maximum 10.957 12.865 28.860 13.128 15.990 20.817 16.178 12.589 Minimum -9.470 -10.007 -17.905 -12.732 -11.809 -24.153 -13.089 -17.231 Std.Dev 1.192 1.298 2.120 1.544 1.603 1.374 1.429 1.899 Skewness -0.2366 -0.2346 1.0817 -0.1934 -0.0721 0.4427 0.2547 0.2241 Kurtosis 11.730 9.611 23.687 11.858 8.513 53.111 13.858 10.056 Jarque-Bera 16611 9545 94028 17086 6610 545918 25681 9238 (p-value) (0.

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