AN EVALUATION OF VOLATILITY FORECASTING TECHNIQUES

The existing literature contains conflicting evidence regarding the re lative quality of stock market volatility forecasts. Evidence can be f ound supporting the superiority of relatively complex models (includin g ARCH class models), while there is also evidence supporting the supe riority of more simple alternatives. These inconsistencies are of part icular concern because of the use of, and reliance on, volatility fore casts in key economic decision-making and analysis, and in asset/optio n pricing. This paper employs daily Australian data to examine this is sue. The results suggest that the ARCH class of models and a simple re gression model provide superior forecasts of volatility. However, the various model rankings are shown to be sensitive to the error statisti c used to assess the accuracy of the forecasts. Nevertheless, a clear message is that volatility forecasting is a notoriously difficult task .