Kosapattarapim, Chaiwat; Lin, Yan-Xia; and McCrae, Michael, Evaluating the volatility forecasting performance of best fitting GARCH models in emerging asian stock markets, Centre for Statistical and Survey Methodology, University of Wollongong, Working Paper 13-11, 2011.
Problem statement : While modeling the volatility of returns is essential for many areas of finance, it is well known that financial return series exhibit many non-normal characteristics that cannot be captured by the standard GARCH model with a normal error distribution. But which GARCH model and which error distribution to use is still open to question, especially where the model that best fits the in-sample data may not give the most effective out-of-sample volatility forecasting ability which we use as the criterion for the selection of the most effective model from among the alternatives. Approach: In this study, six simulated studies in GARCH (p,q) with six different error distributions (normal, skewed normal, student-t, skewed student-t, generalized error distribution and skewed generalized error distribution) are carried out. In each case, we determine the best fitting GARCH model based on the AIC criterion and then evaluate its out-of-sample volatility forecasting performance against that of other models. The analysis is then carried out using the daily closing price data from Thailand (SET), Malaysia (KLCI) and Singapore (STI) stock exchanges. Results : Our simulations show that although the best fitting model does not always provide the best future volatility estimates the differences are so insignificant that the estimates of the best fitting model can be used with confidence. The empirical application to stock markets also indicates that a non normal error distribution tends to improves the volatility forecast of returns in the presence of heavy-tailed, leptokurtic and skewness. Conclusion : The volatility forecast estimates of the best fitted model can be reliably used for volatility forecasting. Moreover, the empirical studies demonstrate that a skewed error distribution outperforms other error distributions in terms of out-of-sample volatility forecasting.