Forecasting China′s Stock Market Volatility Using Non-Linear GARCH Models

This paper studies the performance of the GARCH model and two of its non linear modifications to forecast China′s weekly stock market volatility. The models are the Quadratic GARCH and the Glosten, Jagannathan and Runkle models which have proposed to describe the often observed negative skewness in stock market indices. We find that the QGARCH model is best when the estimation sample does not contain extreme observations and that the GJR model cannot be recommended for forecasting.