Volatility modeling based on GARCH-skewed-t-type models for Chinese stock market - Fei Lin (2019)
As an emerging stock market with enormous potential, Chinese stock market has apparent volatility clustering appearance along with typical feature of leptokurtic, negative skewness and fat tail in its index yield series. The model based on traditional normal distribution often underestimate the risk, which would lead to profound loss for the investors and financial institution when the extreme events happened. VaR(Value at Risk), which measures risk as a certain value, is widely used in financial industry for its intuitive and concise characteristics. Since parameter method of the VaR calculation is the mostly implementation in practice, the choice of appropriate probability distribution function and variance can quite improve its accuracy. Therefore, the conditional variance is estimated by GARCH-type models and the assumption of normal distribution is replaced by skewed-t distribution. Compared with the common RiskMetrics based on normal distribution, the ARMA-GJR-GARCH-skewed-t model has better adaptability and precision for the VaR estimation of indices of Chinese stock markets.