Value-at-Risk Analysis for the Chinese Stock Market

Recently, it has become a very important task to measure exposure risk accurately in financial market investments. In this paper, we investigate the relevance of the skewed Student’s t distribution innovation in capturing long-memory and asymmetry features in the volatility of Chinese stock markets. We also examine the performance of in-sample and out-of-sample value-at-risk (VaR) analyses using the FIAPARCH model with the normal, Student’s t, and skewed Student’s t distribution innovations. The results from the FIAPARCH model estimation suggest that returns of the Chinese stock markets exhibit long-memory and asymmetry features in volatility. In the in-sample and out-of-sample analyses, the FIAPARCH VaR models with the skewed Student’s t innovation predicted critical loss more accurately than did the models with the normal and Student’s t innovations for both long and short positions. Therefore, risk managers and portfolio investors can estimate VaR and optimal margin levels most accurately by using the skewed Student’s t FIAPARCH VaR models of long and short trading positions in the Chinese stock market.