Modeling and forecasting the asset prices volatility based on high-frequency

This paper considers the application of price jump information in modeling and forecasting the volatility. We use the method in Corsi et al. (2010) to separate the asset price jump information more effectively, and use the HAR-RV-CJ volatility model containing that information to model and predict the volatility of asset prices. From our research of price volatility of Shanghai Composite Index and Shenzhen Component Index we found that HAR-RV-CJ model which contains jump information is more excellent in predicting out-of-sample prices than other volatility models like HAR-RV, ARFIMA, GARCH and SV.