Measuring Return and Volatility Spillovers in Global Financial Markets
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MEASURING RETURN AND VOLATILITY SPILLOVERS IN GLOBAL FINANCIAL MARKETS PONGSAKORN SUWANPONG1 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 “flow- oriented” 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