MEASURING RETURN AND VOLATILITY SPILLOVERS IN GLOBAL FINANCIAL MARKETS

MEASURING RETURN AND VOLATILITY SPILLOVERS IN GLOBAL FINANCIAL MARKETS PONGSAKORN SUWANPONG 1 FACULTY OF ECONOMICS, CHULALONGKORN UNIVERSITY BANGKOK, THAILAND Abstract This paper purposely measures return and volatility spillovers in global financial markets, currency and equity markets, by employing the variance decomposition of a vector autoregression (VAR) and calculating into spillover indices from January 1998 to June 2010, proposed by Diebold and Yilmaz (2009). The empirical finding suggests that approximately 30 percent of forecast error variance comes from currency market spillovers, both returns and volatilities, while the return and volatility spillovers in equity market are roughly 45 and 55 percent, respectively. In particular, in the static analysis of global equity market, the Straits Times, the Hang Seng and the Australian Securities Exchange are the major sources of return spillover, while the Hang Seng, the FTSE Bursa Malaysia Kuala Lumpur Stock Exchange and the Stock Exchange of Thailand are the significant markets spilling over innovations to other markets. In currency market, the return and volatility spillovers come from Hong Kong dollar (HKD), Indonesian rupiah (IDR), Australian dollar (AUD) and US dollar (USD). Besides, employing the rolling window framework provides the dynamic perspective of global financial market situations. The author found that the volatility spillovers burst across markets during a major crisis, whereas the return spillovers perform steady trends over time. Specifically, the return spillover reaches the highest level in the period of the global financial market turmoil during 2008 2009, while the volatility spillover jumps in most of financial crises across time. Keyword: Currency market, Equity markets, Return spillover, Volatility spillover, Vector autoregression, Variance decomposition. I. Introduction The global foreign exchange activities have expedited in recent two decades on account of rapid globalization and integration of world financial markets driven by the development of information technology. Consistent with globalization, the speedy economic liberalization of the international trade and financial markets, conjointly the adoptions of floating exchange rate regime by industrialized countries in the early 1970s have made cross-border capital flows swift and effortless. This evolution has heralded an era of increased exchange rate risk and volatility in global currency market. These 1 Senior student at Bachelor of Economics, Faculty of Economics, Chulalongkorn University, Thailand. The author is truly indebted to Pongsak Luangaram, Ph.D., for inspiration in the research area and his precious, generous guidance. The author is also thankful to Nath Banditwattanawong, Ph.D. student, for his worthwhile suggestion in econometrics.

developments also indicate multiply occurrences of foreign exchange rate volatility spillovers and transmissions across currency markets. For comprehensive perspective, Yang and Doong (2004) indicate that an equity market should be sensitive to the increasing volatility of exchange rates. Besides, as a result of the economic deregulation and integration in the global financial market since the 1980s, currency markets are more responsive to global portfolio investments and innovations in equity market as well. In relevant literature, there are two forms of theoretical linkages between stock prices and exchange rates. Firstly, Mundell (1963,1964) and Dornbusch and Fisher (1980) showed the floworiented models of exchange rate determination which assumed that the exchange rate is determined largely by a country s current account or trade balance performance. These models posited that changes in exchange rates had effects on international competitiveness and trade balance, which further influenced real economic variables such as real income and output. As a result, flow-oriented models represent a positive relationship between stock prices and exchanges rates with a direction of causation running from exchange rates to stock prices. Causation can be explained as follows; domestic currency depreciation makes the local firms more competitive, so their exports become cheaper in international comparison. Higher exports lead to higher incomes and increase in firms stock prices. On the other hand, Branson (1983) and Frankel (1983) gave the stock-oriented models of exchange rate determination which they put much stress on the role of financial account in determining exchange rate dynamics. In these models, exchange rates are viewed as equating the supply and demand for assets such as stocks and bonds. Expectations of relative currency values play a crucial role in their price movements, especially for internationally held financial assets, because the values of financial assets are defined by the present values of their future cash flows. Therefore, exchange rate dynamics may be affected by stock price innovations, which means, in other words, that causation runs from stock prices to exchange rate changes. From previous studies examining the relationship between stock and foreign exchange market mainly for US, they provided different results of the linkages between these two markets. For instance, Aggarwal (1981) noticed that the revaluation of the US dollar is positively related to stock market returns. On the other hand, Soenen and Hennigar(1988) found a significantly negative relationship by considering a different period, 1980-1986. Besides, Roll (1992) using daily data over the period 1988-1991 also found a positive relationship between the two markets. In contrast, Chow et al (1997) found no relationship for monthly excess stock returns and real exchange rate returns by using monthly data for the period 1977-1989. Nevertheless, after repeating the exercise with longer than six months horizons, they found that there is a positive relationship between a strong dollar and stock returns. Moreover, Yang and Doong (2004) states that, in spite of examining the linkages and interactions between exchange rates and stock prices, only a limited body of the paper has attempted to analyze the possibility that volatility spillover effect can occur between the stock and currency markets. The understanding of information transmission between stock prices and exchange rates is expanded by an examination of the volatility spillover process.

On the other hand, apart from the previous studies of linkage between the currency and equity markets, many methods used to calculate the volatility spillover in financial markets gave some different results which will be described in the following. The fundamental methodology uses correlation analysis method in the study of volatility spillover across financial markets. Using this methodology, Baig and Goldfajth (1999) found that cross market correlation increased during the crisis. Forbes and Rigobon (2001) discovered that there were no contagions; however, only interdependence in cross-country equity markets is found. Besides, there is no increase in correlation, assuming that Hong Kong is the dominant market. The next methodology is autoregressive conditional heteroskedasticity, GARCH, which can be divided into various types. Dungey and Martin (2007), using GARCH in order to study the volatility spillover, indicated that during the crisis, spillover and contagion effects are distinguishable. In et al. (2001) processed another type of GARCH, which is VAR-EGARCH. The result showed that there was a unidirectional volatility transmission from Korea to Thailand and Hong Kong. In addition, GJR- GARCH employed by Fernandez and Lafuente (2004) provided the results that leverage effect existed not only due to negative shocks but also to shocks in foreign markets. The other methodology is Probit Models used by Forbes (2004) and Kaminsky and Reinhart (1999). Frobes (2004) suggested that trade links were the most important transmission mechanism. Kaminsky and Reinhart (1994) posited that the probability of a crisis increased when more crises occurred in other countries, especially in the same geographical area. In addition, the volatility spillover can be measured by using the variance decomposition of a vector autoregression (VAR) and calculating into spillover index, proposed by Diebold and Yilmaz (2009). The n-step-ahead forecast in the variance decomposition in the spillover index is not only easy and intuitive, but also gives both the static and dynamic perspectives of the behaviors in returns and volatilities. This paper purposely measures return and volatility spillovers in global financial markets, currency and equity markets, by separately employing the variance decomposition of a vector autoregression (VAR) of returns and return volatilities and calculating into spillover indices, proposed by Diebold and Yilmaz (2009). In static analysis, the objectives of the paper is to investigate not only the size and sources of return and volatility spillovers in foreign exchange market, which may arise from regional or major trading partners currencies, but also to clarify the sources of return and volatility spillovers in equity market. In dynamic analysis, using a rolling window framework enables us to account for major changes in the return and volatility spillovers, reflecting in the economy over time. Furthermore, the full-sample currency and equity markets are selected from both advanced countries and emerging economies. The advanced economies are The United States of America, Germany, France, Australia, and Japan, while the selected emerging markets are Hong Kong, the Republic of Korea, Indonesia, Malaysia, Philippines, Singapore, and Thailand. Hence, these are 12 national equity market indices and 11 direct-quoted foreign exchange rates acquired from January 1998 to June 2010.Thereby, the empirical findings in both static and dynamic analysis are essential for international portfolio investors devising portfolio strategies. Moreover, the policy implications are particularly important to policymakers for the interventions of the currency market and the regulations in the equity market.

The remainder of the paper is organized as follows. The next section exhibits the data and methodological issues, the spillover index. Section III is the empirical findings in the global financial markets that consist of the empirical analysis of spillover tables in static analysis and spillover plots in dynamic analysis. Finally, section IV summarizes. II. Data and Methodology i. Data The underlying data sets consist of daily equity market indices for 12 countries and foreign exchange rates for 11 countries, January 1998 to June 2010, in opening, closing, high, and low values. Each equity and currency market is selected from both advanced economies and emerging countries, which can obtain the fundamental cross-country global information. The advanced countries are the United States of America, Germany, France, Australia, and Japan. While the selected emerging markets are Hong Kong, the Republic of Korea, Indonesia, Malaysia, Philippines, Singapore, and Thailand. The national equity market indices for 12 countries are S&P/ASX 200 Index, CAC 40 Index, DAX Index, HSI Index, JCI Index, NKY Index, FBMKLCI Index, PCOMP Index, STI Index, SET Index, KOSPI Index, and NYSE Index. The direct-quoted, or per US Dollar, foreign exchange rates are Australian dollar (AUD), the Euro (EUR), Hong Kong dollar (HKD), Indonesian rupiah (IDR), Japanese yen (JPY), Malaysian ringgit (MYR), Philippine peso (PHP), Singapore dollar (SGD), Thai baht (THB), South Korea won (KRW), and trade-weighted U.S. dollar index (DXY) (see Appendix). The dataset in equity market is daily nominal local-currency composite stock market indices, January 1998 to June 2010, obtained from Bloomberg, and the dataset in currency market is daily directquoted foreign exchange rates, January 1998 to June 2010, acquired from Reuters. The daily data is aggregated into weekly observations by using the annualized weekly percent return and efficient rangebased volatility estimate formulas (see Methodology). ii. Methodology A statistical analysis of returns and volatility is employed to study currency and equity market spillovers. The weekly returns are measured by using underlying equity index level and foreign exchange rates at the Friday closing price, and expressed as annualized percentages. The annualized weekly percent return for market i is Closing prices on each Friday represent weekly closing observations. If Friday observation is absent, a corresponding nearby value in Thursday is used.

Volatility is assessed following the efficient range-based volatility estimate proposed by Garman and Klass (1980), which incorporates the stock market information available for investors low, high, opening, and closing prices obtained from underlying daily high, low, opening and closing data (from the Monday open to the Friday close). Apparently, this estimator is more efficient, specifically named the best analytic scale-invariant volatility estimator, than the classical estimation since the estimator required the process of continuous value observations that automatically yield to a large amount of information collected and employed. As a consequence, weekly volatility is assumed constant over a week and is modeled according to the following rule: Where is the maximum of all high prices over a week, is the minimum of all low prices over a week, is the opening on Monday, is the closing on Friday. The next step is to calculate return and volatility spillover indices in accordance with the Diebold and Yilmaz (2009) methodology. Indices are estimated by means of linear VAR models Where is a vector of weekly returns or volatilities, s is the number of currency or equity markets, and p is the order of the model and the Cholesky decomposition of a forecast error variance. A spillover index is defined as a summary effect attributable to external shocks over total forecast error variance of the system in percent. Hence, the spillover index for the n-stepahead forecast is represented as Where is an innovation matrix. III. Empirical Results: the Return and Volatility Spillovers in Financial Markets i. Descriptive Statistics The datasets in both currency and equity markets for 12 countries, 12 daily equity market indices and 11 foreign exchange rates from January 1998 to June 2010, are used in weekly return and weekly volatility forms, and the dataset is tested for unit root stationary. There are descriptive statics:

Descriptive statistics table 1, global currency market returns, January 1998 - June 2010 AUD EUR HKD IDR JPY MYR PHP SGD THB KRW DXY Mean 2.72193 1.53044 0.01275 3.15267-3.468-1.842 0.73316-1.817-3.193-2.995 0.01785 Median 9.81303 4.95756 0.06644 0.00000 1.34344 0.00000 1.85024-1.562-1.492-3.603 0.00000 Maximum 462.400 289.911 19.5424 2918.94 308.887 785.758 380.807 242.880 476.827 402.821 13.9137 Minimum -712.7-339 -33.45-2055 -781.2-533.5-492.1-358 -573.2-714.8-35.46 Std. Dev. 98.3931 74.4483 3.51549 216.946 88.5112 69.2091 62.5777 43.4660 70.2046 93.3222 2.79199 Skewness -1.015-0.257-2.458 3.813264-1.302 0.750608-0.378-0.667-0.439-1.012-2.925 Kurtosis 10.1345 4.38014 29.5869 78.86813 12.23363 51.46621 15.68304 12.53998 22.32691 15.27854 45.95454 Observations 657 657 657 657 657 657 657 657 657 657 657 Descriptive statistics table 2, global currency market volatilities, January 1998 - June 2010 AUD EUR HKD IDR JPY MYR PHP SGD THB KRW DXY Mean 0.000194 0.000318-0.03542-605891 3.528118-1.07191-5.24182 0.000222 0.129778-4519.49 877.8132 Median 0.000103 0.000217 1.13E-05 1.20E+04 2.183503 5.58E-06 1.56E-01 0.000101 0.076591 104.8349 810.6634 Maximum 0.006036 0.008022 0.003956 10440628 153.7323 0.256435 36.56051 0.007424 26.42372 31597.01 1963.889 Minimum 9.20E-06 1.52E-05-23.2971-5.5E+07 0.304359-5.53056-1008.08 1.16E-05-4.8E+02-728639 449.0883 Std. Dev. 0.000369 0.000448 0.908909 5663788 7.349834 2.169996 73.90934 0.000496 19.22356 51625.6 271.4661 Skewness 8.549589 9.795001-25.5735-6.81762 14.55452-1.52849-12.718 8.073162-24.8572-10.6276 0.653373 Kurtosis 110.2712 148.0245 655.0014 51.39832 276.6764 3.351931 163.1474 92.21303 631.1709 121.6478 2.630701 Observations 657 657 657 657 657 657 657 657 657 657 657

Descriptive statistics table 3, global equity market returns, January 1998 - June 2010 ASX CAC DAX HSI JCI NKY FBMKL PCOMP STI SET KOSPI NYA Mean 4.417877 1.681423 2.898011 6.826293 17.30358-3.820662 8.054736 6.542523 7.246141 7.142687 10.89605 2.263719 Median 15.38463 10.73697 21.30353 13.90834 23.70167 8.457097 13.46474 8.537625 9.915146 24.51420 31.92103 16.92239 Maximum 473.9135 646.4705 776.9917 723.6790 977.7546 595.3818 1278.085 841.6006 1033.422 821.3790 885.6591 630.6466 Minimum -884.8451-1302.621-1266.043-926.4014-1211.449-1449.989-595.3135-1048.013-856.3546-1386.391-1192.298-1130.196 Std. Dev. 113.0722 169.5515 185.9915 187.9535 216.0141 167.4445 159.5414 184.8174 173.2756 203.4555 230.5758 141.6996 Skewness -1.088966-0.865674-0.607055-0.136009-0.343308-1.141457 0.863035-0.034653 0.132496-0.516320-0.311189-0.977612 Kurtosis 9.950174 9.107786 7.585141 4.772231 6.933951 11.55478 12.20853 6.415778 7.738630 7.796357 5.084233 11.07786 Observations 657 657 657 657 657 657 657 657 657 657 657 657 Descriptive statistics table 4, global equity market volatilities, January 1998 - June 2010 ASX CAC DAX HSI JCI NKY FBMKL PCOMP STI SET KOSPI NYA Mean 7104.621 17181.55 29610.41 267887.5 1864.580 134306.8 387.8135 3151.629 3446.379 289.6130 1352.002 27419.73 Median 1963.530 10041.72 16327.94 114432.2 439.1124 86616.41 171.8547 1557.063 1672.119 156.8687 726.6188 12477.31 Maximum 225454.2 211849.0 403072.1 6156041. 76592.09 1679604. 8777.990 56149.58 68574.42 10085.34 23476.16 788557.9 Minimum 17.85856 362.8086 603.1116 2966.316 8.907385 2166.465 7.704600 31.70845 132.7431 9.361674 23.58703 469.9247 Std. Dev. 16470.54 22037.75 41052.78 526648.3 4911.568 161866.8 771.8562 4992.201 6055.511 539.9890 2122.409 56238.21 Skewness 6.642705 3.789690 4.423222 6.215393 8.321417 3.817301 6.700356 4.861916 5.738600 10.63937 5.621906 7.784860 Kurtosis 64.93087 23.91404 30.62568 54.81366 102.9970 24.98658 60.61420 37.50670 47.01093 173.4848 45.77142 83.99084 Observations 657 657 657 657 657 657 657 657 657 657 657 657

ii. Static Analysis: Spillover Tables This is a full-sample analysis of 12 countries of currency and equity market return and volatility spillovers. As part of the analysis, the author follows decomposing the spillover index into all of the forecast error variance components for variable i coming from shocks to variable j, for all i and j proposed by Diebold and Yilmaz (2009). Firstly, the author characterizes return and volatility spillovers over the entire sample from January 1998 to June 2010. The author reports spillover indexes for returns and volatilities of currency and equity markets in the lower right corners of spillover tables 1 to 4, respectively. The ij-th entry in the table is the estimated contribution to the forecast error variance of country i (returns in currency market in spillover table 1, volatilities in currency market in spillover table 2, returns in equity market in spillover table 3, and volatilities in equity market in spillover table 4) coming from innovations to country j. As a consequence, the off-diagonal column sums (named To Others) when totaled across countries, bestow the numerator of the spillover index. While, the column sums or row sums, which are including diagonals, provide the denominator of the spillover index. The important empirical findings in currency market from spillover tables 1 and 2 are that approximately 30% of forecast error variance comes from spillovers, both for returns (30.92%) and volatilities (27.67%). Besides, there is roughly 50% of forecast error variance comes from equity spillover. Hence, in equity market, spillovers are important in both returns (46.35%) and volatilities (56.78%), and on average return and volatility spillovers in currency market are of the same magnitude, unconditionally. However, at any given point in time, conditionally, return and volatility spillovers may be very different and, more generally, their dynamics may be different (see Empirical results in spillover plots). Spillover table 1: global currency market returns, January 1998 - June 2010 FROM TO AUD DXY EUR HKD IDR JPY KRW MYR PHP SGD THB Contribution From Others AUD 95.97 0.28 1.27 0.08 0.38 0.26 1.02 0.21 0.45 0.01 0.09 4.03 DXY 2.34 58.45 1.09 35.95 0.26 0.28 0.31 0.84 0.11 0.11 0.27 41.55 EUR 26.99 0.48 69.79 0.19 0.15 0.57 0.86 0.65 0.21 0.02 0.09 30.21 HKD 3.82 7.95 1.68 84.11 0.25 0.05 0.07 0.9 0.28 0.24 0.64 15.89 IDR 3.00 0.34 0.2 0.08 80.3 0.28 0.78 12.68 1.25 0.84 0.24 19.70 JPY 0.68 0.41 9.17 1.21 2.05 85.17 0.14 0.35 0.16 0.34 0.32 14.83 KRW 17.51 0.37 0.74 0.35 9.78 0.99 64.65 3.87 1.14 0.29 0.30 35.35 MYR 6.52 0.39 0.81 0.87 20.32 1.01 3.23 65.87 0.74 0.06 0.17 34.13 PHP 5.42 0.36 0.32 0.35 10.56 1.21 6.20 4.27 69.8 1.07 0.45 30.20 SGD 27.61 0.45 7.53 0.82 12.82 5.67 2.75 7.42 1.88 32.77 0.28 67.23 THB 6.06 0.33 1.41 0.39 14.22 2.26 2.38 11.73 2.81 5.43 52.97 47.03 Contribution To Others 195.93 69.81 94.01 124.4 151.09 97.75 82.39 108.79 78.83 41.19 55.83 Spillover Index = 30.92 %

Spillover table 2: global currency market volatilities, January 1998 - June 2010 FROM TO AUD DXY EUR HKD IDR JPY KRW MYR PHP SGD THB Contribution From Others AUD 97.26 0.34 0.45 0.05 0.15 0.17 0.65 0.06 0.05 0.70 0.10 2.74 DXY 5.20 88.90 0.17 3.28 0.18 0.87 0.35 0.72 0.11 0.16 0.04 11.10 EUR 19.60 1.98 69.21 0.10 5.87 0.95 0.29 0.22 1.39 0.36 0.02 30.79 HKD 0.05 13.69 0.98 83.03 0.01 0.01 0.30 1.80 0.01 0.09 0.03 16.97 IDR 3.90 2.02 10.56 6.09 71.03 0.29 2.25 0.71 2.79 0.07 0.29 28.97 JPY 14.67 0.43 0.31 0.09 0.14 77.56 0.26 0.07 0.04 6.40 0.02 22.44 KRW 1.96 8.17 11.49 28.44 2.37 0.37 42.26 1.16 3.04 0.27 0.46 57.74 MYR 0.48 1.00 0.64 0.71 3.41 0.02 0.67 92.57 0.30 0.09 0.11 7.43 PHP 0.02 3.37 0.15 18.49 16.61 0.06 0.49 1.03 58.08 0.03 1.67 41.92 SGD 6.22 0.35 4.78 0.14 0.39 9.18 0.13 0.04 0.15 78.58 0.04 21.42 THB 0.02 0.26 0.13 0.10 13.87 0.07 2.55 0.66 45.21 0.02 37.11 62.89 Contribution To Others 149.39 120.51 98.86 140.53 114.01 89.57 50.21 99.07 111.17 86.79 39.89 Spillover Index = 27.67 % The spillover table 1 and 2 illustrate the full-sample, from January 1998 to June 2010, of global currency market returns and volatilities, respectively. Both return and volatility spillovers are quite sizable; return spillovers are 30.92 percent, and volatility spillovers are 27.67 percent. All in all, the global currency market spillovers have almost the same size for both returns and volatilities, approximately 30 percent, which means that innovations to one regional currency market have one-third impact on the others. In Asian regional perspective, the return spillover comes predominantly from Hong Kong dollar (HKD), Indonesian rupiah (IDR) and Australian dollar (AUD), while currency market volatility spillover substantially results from Australian dollar (AUD), US dollar (USD), Hong Kong dollar (HKD) and Indonesian rupiah (IDR). For Thailand, Thai baht (THB) return spillover is mainly from regional currencies those are Indonesian rupiah (IDR), Malaysian ringgit (MYR) and Singapore dollar (SGD). Moreover, the volatility of Thai baht (THB) arises from Indonesian rupiah (IDR), Philippine rupiah (PHP) and South Korea won (KRW). The global currency spillovers imply that foreign exchange rates of the United States of America, Australia, Hong Kong and Indonesia plays a crucial role in the global currency spillovers.

Spillover table 3, global equity market returns, January 1998 - June 2010 TO FROM ASX CAC DAX FBMKL STI HIS JCI KOSPI NKY NYSE PCOMP SET Contribution From Others ASX 81.93 3.54 0.69 0.06 9.94 0.31 0.15 0.14 0.04 2.20 0.23 0.77 18.07 CAC 37.86 52.44 0.39 0.06 5.70 0.38 0.38 0.65 0.31 0.29 0.32 1.22 47.56 DAX 31.45 43.72 17.35 0.30 4.30 0.48 0.51 0.40 0.21 0.22 0.16 0.90 82.65 FBM KL 9.47 1.67 2.14 69.18 1.88 6.17 4.06 0.39 3.25 0.34 0.08 1.37 30.82 STI 15.43 4.16 0.82 1.74 58.74 11.24 1.83 1.49 0.74 0.21 2.68 0.92 41.26 HIS 28.89 6.20 2.00 0.15 6.83 52.63 0.21 0.14 0.43 1.29 0.25 0.98 47.37 JCI 12.40 1.94 0.91 0.37 6.18 3.13 70.82 1.40 0.03 1.94 0.22 0.68 29.18 KOSPI 17.69 4.76 2.31 0.32 5.51 8.72 2.36 55.16 1.06 1.28 0.05 0.78 44.84 NKY 29.78 6.08 1.30 0.22 6.28 2.15 1.55 2.22 47.97 1.43 0.60 0.43 52.03 NYSE 37.83 20.43 1.75 0.62 9.62 0.70 0.14 0.27 0.70 26.36 0.40 1.16 73.64 PCOMP 17.93 1.16 0.61 1.91 4.37 3.81 3.88 0.23 0.50 1.95 61.76 1.88 38.24 SET 14.80 2.23 2.40 2.37 6.71 6.00 6.50 3.03 0.09 3.36 3.05 49.46 50.54 Contribution Spillover Index To Others 335.46 148.32 32.68 77.30 126.05 95.73 92.40 65.52 55.33 40.86 69.81 60.55 = 46.35 % Spillover table 4, global equity market volatilities, January 1998 - June 2010 TO FROM ASX CAC DAX FBMKL STI HIS JCI KOSPI NKY NYSE PCOMP SET Contribution From Others ASX 50.21 1.49 1.00 7.76 3.67 4.21 4.80 3.23 0.38 0.30 1.39 21.57 49.79 CAC 6.86 66.27 1.07 6.59 0.77 3.32 2.72 1.12 1.25 0.37 0.62 9.04 33.73 DAX 7.47 44.17 14.47 8.54 1.17 3.69 3.33 1.78 0.83 0.54 1.02 13.00 85.53 FBM KL 11.62 11.68 1.25 34.76 1.20 5.38 8.06 2.10 0.34 0.23 0.50 22.87 65.24 STI 14.83 1.65 1.19 4.84 38.58 5.59 3.91 3.84 0.71 1.59 1.85 21.42 61.42 HIS 17.87 4.44 2.34 6.68 1.06 28.12 2.67 4.84 0.41 0.62 0.72 30.24 71.88 JCI 31.70 1.04 0.88 1.85 1.19 6.91 24.01 3.37 0.90 1.10 1.06 25.97 75.99 KOSPI 6.45 6.57 1.44 22.10 1.28 4.17 5.00 28.15 0.17 0.61 0.91 23.15 71.85 NKY 3.47 11.40 4.76 14.96 1.19 2.90 3.97 1.75 46.11 0.34 2.44 6.72 53.89 NYSE 2.74 0.89 0.72 0.82 0.17 2.34 1.06 1.32 0.27 81.47 0.94 7.25 18.53 PCOMP 8.30 3.46 2.22 1.57 5.47 2.44 1.27 7.13 5.59 0.62 54.62 7.32 45.38 SET 2.79 6.88 1.49 8.01 3.68 9.61 5.44 5.19 0.71 0.31 4.05 51.84 48.16 Contribution Spillover Index To Others 164.33 159.95 32.83 118.47 59.43 78.67 66.23 63.82 57.66 88.12 70.12 240.37 = 56.78 %

The spillover table 3 and 4 show the full-sample from January 1998 to June 2010, of global equity market returns and volatilities, respectively. The equity return and volatility spillovers are considerable; return spillovers are 46.35 percent, and volatility spillovers are 56.78 percent. On the whole, the size of the global equity volatility spillover is far larger than the return spillover s, which means that innovations in one national equity market represent the perception of risk, volatility spillover, far greater than the perception of returns to other national equity markets. In the comparison with return spillover in currency market, the equity return spillover index has greater magnitude owing to the higher in liquidity of the assets in the foreign exchange market than in stock market. Moreover, in Asian regional aspect, the equity return spillover arises mostly from the Straits Times, the Hang Seng and the Australian Securities Exchange, whereas equity volatility spillover particularly originates from the FTSE Bursa Malaysia Kuala Lumpur Stock Exchange and the Stock Exchange of Thailand. Furthermore, for the Stock Exchange of Thailand, the equity return spillover is mainly from regional equity markets that are the Straits Times, the Hang Seng and the Jakarta Composite Index. However, the volatility spillovers affecting the Stock Exchange of Thailand are the FTSE Bursa Malaysia Kuala Lumpur Stock Exchange, the Hang Seng, the Jakarta Composite Index and the Korea Composite Stock Price Index. These global equity spillovers imply that most of the stock markets in ASEAN fluctuate from both the FTSE Bursa Malaysia Kuala Lumpur Stock Exchange and the Stock Exchange of Thailand. iii. Dynamic Analysis: Spillover Plots In the dynamic analysis, the author applies the rolling window framework by using 200-week sub-sample rolling windows in order to capture cyclical movements in spillovers, which represent the global financial market situations. For each sub-sample window, the spillover index in currency or equity markets is calculated and demonstrated the contribution of spillovers across markets to the forecast error variance. Plotting the spillover indices in financial markets across time the author acquires dynamic perspectives of return and volatility spillovers across markets, reflecting in the global economy over time, which can be examine graphically in the spillover plots.

Spillover Index 70 Figure 1: Return Spillover Plot 60 50 Global financial market turmoil 40 30 20 10 0 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Currency Market Equity Market Spillover plots for returns and volatilities are presented in spillover plot figures 1 and 2, respectively. To begin with the return spillovers, in the broad point of view, the return spillover in equity market is higher than the spillover in currency market as the liquidity characteristic of the assets in the stock market is limited. Considering in details, after the aftermath of Asian crisis, the return spillover started at roughly 35% (in currency market) and 40% (in equity market) of innovations to financial index returns spilled over across markets. The return spillovers moved rather steadily from 1998 to 2006, although there were major distresses in global economy in this episode, for instance, the dot-com bubble in 2001, the Iraq war in 2003, the reversal in the Federal Reserve interest rate policy in 2004-2005, and the Indonesian mini crisis in 2005. However, the return spillovers increased in late 2006, which were possibly affected by the capital outflows from emerging markets. In addition, subsequent to a decade of variation within band of 40-50%, the return spillover index in equity market surged drastically to 63% in August 2007 since the first sign of the US sub-prime crisis impacted other major equity markets. Also, the return spillover in the currency market jumped significantly to approximately 45% in late 2007 after the steady trend within the 25-35% band. Furthermore, the return spillovers in both markets were still at the soaring level that signified the global financial market turmoil of 2008-2009 and asserted the interdependency among cross-country markets, both currency and equity markets. All in all, the return spillover generally behaves the steady trend across time except the global financial market turmoil.

Spillover Index 80 Aftermath of Asian currency crisis 70 Dot-com bubble 60 Figure 2: Volatility Spillover Plot Capital outflows from emerging markets Global market chasing U.S. stock down Reversal in FED interest rate policy stance Iraq war First sign of subprime crisis Global financial market turmoil Currency war 50 40 30 Indonesian mini crisis 20 10 0 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Currency Market Equity Market Nevertheless, the volatility spillovers plot in Figure 2 is dissimilar to the return spillover plot in Figure 1. The return spillovers moved somewhat smoothly across time with occasional fluctuations during two major crises, while the volatility spillover index jumped in major financial innovations or crises. To begin with, after East Asian crisis in 1998, the volatility spillover index in equity market became somewhat stable around 55% for almost three years. While the volatility spillover in currency market decreased significantly as a result of the adoption of the floating exchange rate regime in East Asian countries. Then, the volatility spillovers in both currency and equity markets surged to more than 55% in dot-com bubble following by an increase in volatility spillover indices in mid 2002 in the global market chasing US stock slump. Moreover, the innovations and news of the Iraq war in 2003 had slight impacted on global equity market but did not affect the volatility spillover index in global currency market. In early 2004, the spillover indices were bursts in consequence of the reversal in Federal

Reserve interest rate policy stance and reached a peak at approximately 45% and 55% in currency and equity markets, respectively. Besides, the volatility spillover index was also affected and changed around 7 percent in Indonesian mini crisis 2005, which took place owing to concerns over sustainability of government budget and the depreciation of the Indonesian rupiah. Next, the volatility spillover increased from 2005 to 2006 and brew up to 50% in currency market and 65% in equity market because of the capital outflows from emerging economies that was resulting from FED s decision to increase interest rates in early 2006. In March 2007, the volatility spillover index in equity market mounted at 67% representing the first sign of sub-prime mortgage crisis in the US, which appeared as several financial institutions declared bankruptcy while others stopped providing loans. Subsequent to the subprime crisis, the volatility spillover jumped the most, hitting its 73% in equity market and accrued to approximately 50% in currency market, because of the global financial market turmoil in mid- September 2008. Following the Lehman Brothers collapse in September, the U.S. Treasury decided to prevent the looming collapse of AIG and a possible financial meltdown. Ultimately, the volatility spillover in currency market still augmented from 2009 to 2010 which could be signified by the indication of currency war in global foreign exchange market. IV. Summary In this paper, the author intentionally measures return and volatility spillover indices in 11 crosscountry currency markets and 12 national equity markets from January 1998 to June 2010, by employing the variance decomposition of a vector autoregression proposed by Diebold and Yilmaz (2009). In static analysis, the significant empirical findings in currency market are that approximately 30% of forecast error variance comes from both return and volatility spillovers. Besides, in the global equity market, there are nearly 45% and 55% of forecast error variances coming from return and volatility spillovers, respectively. Considering the currency market in Asian countries, US dollar, Australian dollar, Hong Kong dollar, and Indonesian rupiah are the dominant factors generating the return and volatility spillovers. For Thailand, Thai baht return and volatility spillovers are mainly from regional currencies. Furthermore, in global equity market, the Straits Times, the Hang Seng and Australian Securities Exchange are the primary sources of return spillover, while the Hang Seng, the FTSE Bursa Malaysia Kuala Lumpur Stock Exchange and the Stock Exchange of Thailand are significant markets transmitting volatilities and systematic risks to other markets. In the dynamic analysis, using rolling window framework, the author found that volatility and return spillovers behave in a different way. The return spillovers represent the interdependency among the markets, while the volatility spillovers signify the systematic risk of the current global financial crises. Moreover, the volatility spillovers burst across markets during a major crisis, whereas the return spillovers perform steady trends over time. As a result of increasing market integration throughout the 2000s period, the return spillover reached the highest level during the global financial market turmoil of 2008 2009, while the volatility spillovers jump in most of financial crises across time.

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