Forecasting Chinese stock market volatility with high-frequency intraday and current return information

In this paper, we propose the Real-Time Realized GARCH model incorporating the high-frequency intraday information and current return information simultaneously to model and forecast the Chinese stock market volatility. An empirical application to the Shanghai Stock Exchange Composite Index (SSEC) and Shenzhen Stock Exchange Component Index (SZSEC) of China shows that the model outperforms the GARCH model, the Real-Time GARCH model and the Realized GARCH model in terms of both empirical return fit and out-of-sample volatility forecast. Moreover, robustness analysis demonstrates that the superior out-of-sample predictive power of the Real-Time Realized GARCH model is robust to alternative out-of-sample forecast windows, alternative realized measure as well as alternative forecast horizons. Finally, we show that for a risk-averse investor, incorporating the high-frequency intraday information and current return information into a volatility-timing strategy yields substantial economic benefits. Our empirical findings highlight the value of incorporating both the high-frequency intraday information and current return information for forecasting the Chinese stock market volatility.