Improving the Forecasting Power of Volatility Models

Volatility models have been extensively used in risk modeling especially GARCH models under the normal distribution. Although they generate highly significant coefficient estimates, these models are known to have poor forecasting power. It is therefore interesting to develop a different approach of risk modeling to improve forecasting results. By using the generalized t-distribution in modeling the changes in the distribution of stock index returns, the results show a significant improvement in the forecasting power. Moreover, Monte Carlo simulations have confirmed that the index returns are better explained by ARCH-type models.