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This study has investigated sudden changes of volatility and examined the volatility asymmetry and persistence for the Shanghai and Shenzhen stock market indices. In an effort to assess the impact of sudden changes in volatility asymmetry and persistence, we identify the time points at which sudden changes in volatility occurred, and then incorporate this information into the GARCH and GJR-GARCH models. Using the ICSS algorithm, the identification of sudden changes is largely associated with domestic and global events. When these sudden changes are incorporated into GARCH and GJR-GARCH models, the evidences of asymmetry and persistence has been vanished in the volatility of both markets. Thus, we conclude that sudden changes models in this study are superior to standard GARCH and GJR-GARCH models in calculating and forecasting volatility of Chinese stock markets.