원문정보
초록
영어
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.
목차
1. Introduction
2. Methodology
2.1. Detecting points of sudden change in variance
2.2. GARCH(1,1) and GJR-GARCH models
2.3. Multiple sudden changes with GARCH and GJR-GARCH models
3. Data and descriptive statistics
4. Empirical results
4.1. Sudden changes in variance
4.2. GARCH and GJR-GARCH estimation with and without sudden changes
4.3. Out-of-sample forecasts
5. Conclusions
References