Value-at-Risk Forecasting of Chinese Stock Index and Index Future Under Jumps,Permanent Component,and Asymmetric Information

This article investigates the performance of time series models considering the jumps, permanent component of volatility, and asymmetric information in predicting value-at-risk (VaR). We use evaluation statistics including size and variability, accuracy, and efficiency to determine some suitable VaR measures for the Chinese stock index and its futures. The results reveal that models with jumps can provide VaR series that are less average conservative and have higher variability. Furthermore, additional considering the permanent component of volatility and asymmetric effect can induce more accurate and efficient risk measure in the long and short positions of the stock index and its futures.