Empirical studies show that the return and volatility shocks in the US stock market have significant impact on the return and volatility dynamics of emerging stock markets including India. This study examines whether the spillover effects from the US to Indian stock market display asymmetric characteristics. Precisely, it addresses the following two issues: (1) whether the Indian stock market reacts differently towards positive and negative shocks from the US market, and (2) whether there is a different response pattern of the Indian stock market towards the return and volatility shocks from the US market during the bear and the bull market phases. The study is based on daily closing values of S&P 500 (representing the US market) and S&P CNX Nifty (representing the Indian market) indices from January 1996 to September 2008. The return generating process in both the markets is modeled as AR (1)-TGARCH (1, 1) process. The model describing return generating process in India is augmented to allow asymmetric return and volatility spillover effects from the US market. The results indicate that there is significant response asymmetry in spillover effects both in returns and volatility. Returns in the Indian stock market are more sensitive to negative shocks in the US market rather than the positive shocks. While positive shocks in the US market do not affect the volatility in the Indian stock market, negative shocks significantly increase the volatility. The study does not observe any significant difference in return and volatility spillover during the downward and upward trends in the US market; however, there seems some interaction taking place between stock market trends and asymmetric response to positive and negative shocks.

The growing integration of international financial markets has prompted a vast amount of empirical research to examine the mechanism through which stock market movements are transmitted around the world. The studies have examined how stock returns and volatility in one national stock market influence those of another stock market and their implications for pricing of securities, management of portfolios, hedging and other trading strategies, and for regulatory policies that aim to curb the contagion. The earliest studies on international stock market linkage have focused on the identification of short-term benefits of international portfolio diversification. For example, Ripley (1973), Solnik (1974), and Hilliard (1979) examined the short run correlations of returns across national markets and pointed out the existence of substantial possibilities to diversify internationally taking benefit of low correlation among national stock markets. However, during the 1980s, strong financial linkages among the countries started evolving, particularly due to the process of financial liberalization adopted by most of the developing countries. Increasing level of integration has reduced the benefits of international diversification. Moreover, the empirical studies suggest that the correlation among national stock markets is not symmetric. It increases during the bear phase, further reducing the benefit of diversification when it is more required.

The US stock market plays a role of information leader in the world market. National stock markets world over respond to information signals coming from the US market; hence, there is considerable price and volatility spillovers from the US market to other markets (see for example, Eun and Shim, 1989; and Masih and Masih, 2001). Considering the asymmetric nature of this spillover, the small emerging markets are vulnerable to the contagion effect of the US market during a turbulent period. The knowledge of time-varying co-movements of emerging markets with the US market during different market conditions is of paramount importance both for portfolio managers and regulators in these markets.

Applied Finance Journal, Empirical Research, International Stock Market, Financial Liberalization, Market Volatility, Empirical Evidence, Financial Crisis, Error Correction Mechanism, ECM, Cointegrated Variables, Asymmetric Volatility, Akaike Information Criterion, AIC, Cointegrating Relationship.