سرریز نوسانات بین بازارهای سرمایه چین و بازارهای سرمایه جهان

We propose measures of the directional volatility spillovers between the Chinese and world equity markets based on Diebold and Yilmaz's (2011b) forecast-error variance decompositions in a generalized vector autoregressive framework. It was found that the US market had dominant volatility impacts on other markets during the subprime mortgage crisis. The other markets were also very volatile, and driven by bad news, their massive volatilities were transmitted back to the US market. The volatility of the Chinese market has had a significantly positive impact on other markets since 2005. The volatility interactions among the markets of China, Hong Kong, and Taiwan were more prominent than those among the Chinese, Western, and other Asian markets were. The major correction of the Chinese stock market between February and July 2007 significantly contributed to the volatility surges of other markets. Owing to the restrictions on foreign investment, the Chinese stock market was not considerably affected in terms of market volatility during the subprime mortgage crisis.

As globalization and financial liberalization are enabling international financial markets to become more correlated and connected than ever before, an understanding of the correlations and interactions among various financial markets is crucial for investors, financial institutions, and governments. Early literature focused on the correlations among the financial markets of developed countries (see for example, Eun and Shim, 1989, Hamao et al., 1990 and King et al., 1994). The cited papers show that developed financial markets are interconnected and that the volatility of the US stock market is transmitted to other developed markets. In the last 20 years, with the development of emerging financial markets, financial economists have become increasingly interested in the relationship between emerging and developed markets and its meaning to financial liberalization and global integration. For example, Bekaert and Harvey (1995) estimated the degree of integration between major emerging markets and world equity markets from 1969 to 1992. Bekaert and Harvey (1997) found that capital market liberalization often leads to a higher correlation between local and international markets. Janakiramanan and Lamba, 1998 and Ng, 2000 found that before 1996, volatility in the US and Japanese equity markets spilled over significantly to the stock markets of the Pacific Basin, including Hong Kong, Korea, Malaysia, Singapore, Taiwan, and Thailand. As a young startup, the Chinese stock market has developed at a fast pace. According to statistics from the Chinese Securities Regulatory Commission (CSRC), as of the end of May 2010, the total market value of the Shanghai and Shenzhen stock markets had reached 3.07 trillion USD, ranking third in the world behind the NYSE and the NASDAQ. With this type of growth, many scholars are paying more attention to the correlations between the Chinese and international markets. As one of the first papers on the Chinese stock market, Bailey (1994) found that although some world market indicators could enable the explanation of the characteristics of the Shanghai and Shenzhen markets, these two markets were not integrated with world equity markets in the early 1990s. Furthermore, Wang and Firth (2004) proposed that from 1994 to 2001, the direction of the return spillover was from developed markets such as the US, the UK, and Japan to markets in Greater China, such as mainland China, Hong Kong, and Taiwan. After the 1997 Asian financial crisis, the volatility spillover has been bidirectional between the Greater China markets and the developed markets. Wang and Di Iorio (2007) found little evidence that the Chinese A stock index was better correlated with the MSCI world index, indicating that the Chinese A stock market was isolated from world markets between 1994 and 2004. Lin et al. (2009) argued that from 1992 to 2006, the Chinese A stock market had no significant correlation with world equity markets, whereas the B stock market had some connection with Western markets and more correlation with Asian markets. In the volatility spillover literature, the common econometric methodologies are the multivariate Generalized AutoRegressive Conditional Heteroskedasticity (GARCH), Regime Switching (RS) and Stochastic Volatility (SV) models. The multivariate GARCH model is most commonly used by researchers. Departing from the methods above, Diebold and Yilmaz (2009) provided new measures of return and volatility spillovers of international stock markets based on forecast-error variance decompositions in a vector autoregressive framework (DY 2009). Diebold and Yilmaz (2011a) discussed the return and volatility spillover among five American countries using this method. Yilmaz (2010) used the same method to evaluate the return and volatility spillover among major Asian countries. More importantly, Diebold and Yilmaz (2011b) further improved the DY 2009 method and used the upgraded model (DY 2011) to explore the spillover among major American financial assets including stocks, bonds, foreign exchanges, and commodities from 1999 to 2009, with particular attention to the volatility interaction during the subprime mortgage crisis. Our paper differs from the previous literature in that we are the first to use the DY 2011 framework to shed light on the volatility spillover between the Chinese and world equity markets. The DY 2011 method that we used has several advantages over other models. First, this method does not depend on the Cholesky factor identification of VAR; therefore, the results of variance decomposition do not hinge on the sequence of the variables. Hence, the DY 2011 is superior to the DY 2009. Second, not only can the DY 2011 be used to gauge the magnitude of the volatility spillover, it can indicate the direction of the spillover as well. In other words, it may provide the value of the directional spillover between any two markets, between one market and any set of (regional) markets, or between one market and global (all) markets. Third, as Diebold and Yilmaz pointed out, the DY 2011 avoids the controversial issues associated with the definition and existence of episodes of contagion (see the debate in Forbes and Rigobon (2002)). In addition to exploring the volatility correlation between the Chinese and any other market, as in the previous literature, our paper investigates both the magnitude and direction of the volatility spillover between China and the Greater China markets, between Chinese and the Asian markets, and between China and the global (all) market. This could provide a more vivid picture of the position and power of the Chinese stock market in the world arena. In addition, because we can easily define and measure the direction of the spillover by variance decomposition, we can measure the impact of the Chinese market on any other market or markets, the impact of any market or markets on the Chinese market, and the net impact of the Chinese market on any other market or markets. This distinctive feature provides a better estimation of the directional spillover than a measure of the significance of coefficients under the special variance structures in multivariate GARCH models. Our study also provides a new perspective on the literature on financial integration and liberalization. We discuss the volatility correlation among the Greater China markets, Asian markets and global markets, focusing on the effect of the recent financial crisis on volatility transmission. Diebold and Yilmaz (2009) did not address this issue owing to the limitations of their data and methodology. Although Yilmaz (2010) discussed the correlation among Asian markets and his data range covered this crisis, he only used the DY 2009 model and provided the total spillover index rather than any directional index. Another limitation in his paper is that the DY 2009 method depends on the Cholesky factor identification of VAR; therefore, the results are too sensitive to variable ordering. Adopting the new DY 2011 method allows us to evaluate the volatility correlations among global markets from the perspective of the Chinese stock market. In this respect, our paper could be regarded as a good supplement to Diebold and Yilmaz's (2011b) investigation of volatility spillovers across different assets during the crisis. In our study, we found that from 1996 to 2009, the Chinese stock market was scarcely affected by other markets because it was not completely open to foreign investors. Before 2005, the Chinese market was only slightly affected by other markets. After 2005, the Chinese stock market had a significant volatility spillover to other markets, the net effect of which was positive, indicating that the influence of the Chinese stock market had increased substantially. The volatility interactions among the Chinese, Hong Kong, and Taiwanese markets are more prominent than those among the Chinese, Western, and other Asian markets are; this confirms the effect of financial market integration in the Greater China region. In general, volatility spillovers among the Chinese, Japanese, and Indian markets are more distinctive than those among the Chinese, US, and UK markets are; this indicates that the connections and correlations among Asian stock markets have become increasingly evident in recent years. Regardless of whether it affected or was affected by other markets, the US stock market was highly correlated with other markets in terms of volatility, especially during the subprime mortgage crisis. Other markets were also very volatile, and, driven by bad news, their massive volatilities were transmitted back to the US market. The major correction of the Chinese stock market between February and July 2007 significantly contributed to the volatility surges of other markets. Owing to the restrictions on foreign investment, the Chinese stock market has not been greatly affected in terms of market volatility during the crisis. The remainder of the paper is organized as follows. Section 2 outlines the methodology and model used in this paper. Section 3 introduces the data and provides descriptive statistics. Section 4 presents our substantive results and discussion. Section 5 concludes the paper.

Using the generalized vector autoregressive framework of Diebold and Yilmaz (2011b), wherein forecast-error variance decompositions are invariant to variable ordering, we proposed measures of total volatility spillover, regional volatility spillover, and directional volatility spillover between the Chinese and world equity markets between February 1996 and December 2009. Specifically, we investigated the volatility spillover among 11 world markets, the volatility spillover among the Chinese, Greater Chinese, and Asian markets, as well as the volatility spillover between the Chinese and major individual markets such as stock markets in the US, the UK, Japan, India, Hong Kong, and Taiwan. In our study, we found that from 1996 to 2009, the Chinese stock market was hardly affected by world markets in terms of volatility spillover. Prior to 2005, the Chinese market was slightly affected by other markets. After 2005, the Chinese stock market had a significant volatility spillover effect on other markets, a net effect that was positive, indicating that the influence of the Chinese stock market was greatly enhanced during those years. The volatility interactions among the Chinese, Hong Kong, and Taiwanese markets were more prominent than were those among the Chinese, Western, and other Asian markets, which confirmed the effect of the financial market integration in the Greater China region. In general, volatility spillovers among the Chinese, Japanese, and Indian markets were more distinct than those among the Chinese, the US, and the UK markets, which implies that the connections and correlations among Asian stock markets have become increasingly more evident in recent years. Regardless of whether it affected or was affected by other markets, the US stock market was highly correlated with other markets in terms of volatility, especially during the subprime mortgage crisis. Although other markets were also very volatile, driven by bad news, their massive volatility was transmitted back to the US market. The major correction of the Shanghai stock market between February and July 2007 significantly contributed to the volatility surges of other markets. Owing to restrictions on foreign investment, the Chinese stock market was not greatly affected with respect to market volatility during the recent global crisis. For this study, we used stock indices from 11 markets in Asia, Europe, and North America. As our focus is on the Chinese stock market, we did not consider the stock markets of South America or Australia; this is left for future investigation. Moreover, the DY 2011 framework could be used to shed light on other interesting issues, such as the spillover between financial markets and the macro economy, spillovers among different stock markets within one country, spillovers among cross-listed stocks in various countries, and spillovers with high frequency data.