재무연구 제 권 호 (2010년 5월)
Asian Review of Financial Research, Vol., No..
pp.2197~2217
pp.2197~2217
The impact of sudden changes on volatility persistence and asymmetry in Chinese stock markets
Sang Hoon Kang Assistant Professor, Division of Business Administration, Pusan National University
Seong-Min Yoon Professor, Department of Economics, Pusan National University
This study has investigated sudden changes of volatility and examined the volatility asymmetry and persistence for the Shanghai and Shenzhen 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 occur, and then incorporate this information into the GARCH and GJR-GARCH models. Using the ICSS algorithm, we found that 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. In addition, out-ofsample analysis confirms that volatility models with incorporating sudden changes provide more accurate one-step-ahead volatility forecasts than their counterparts without sudden changes.
Sang Hoon Kang
Seong-Min Yoon
This study has investigated sudden changes of volatility and examined the volatility asymmetry and persistence for the Shanghai and Shenzhen 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 occur, and then incorporate this information into the GARCH and GJR-GARCH models. Using the ICSS algorithm, we found that 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. In addition, out-ofsample analysis confirms that volatility models with incorporating sudden changes provide more accurate one-step-ahead volatility forecasts than their counterparts without sudden changes.
