Conditional Heteroskedasticity in Stock Returns: Evidence from Stock Markets of Mainland China

Abstract

Mainland Chinab s stock markets are becoming more mature and more integrated with the global financial markets. It is worth further exploring not only for investors, but also for policy makers. This thesis investigates various features of conditional heteroskedasticity of stock returns in Shanghai and Shenzhen. It consists of three parts: exploring a more appropriate model to fit the stock returns; studying the dynamics of conditional correlation of returns; and examining the possible regimes by using the Markov-Switching technique. Our findings are reported as follows:First, the fitted ARMA(1,1)-A-PARCH(1,1,1) model with the generalized error distribution is a relatively more suitable one. Second, we find that the conditional correlation between mainland Chinab s and the U.S. stock markets is quite low and highly volatile. Third, we apply a structure of Markov-switching in conditional heteroskedasticity to identify two discrete volatility regimes of Chinab s stock markets and its changing relationship with the U.S. market.