Idiosyncratic Volatility Forecasting in the Stock Market of Saudi Arabia

We test the forecasting ability of two sets of models, one containing historical volatility–based models and the other conditional volatility–based models, on estimates of idiosyncratic risk of individual Saudi Arabian stocks. While the rankings of forecasts are sensitive to the choice of error statistics, historical volatility–based models appear to be superior, unless the model employed to generate the underlying idiosyncratic return series incorporates higher moments. Exponential smoothing models, with a seasonal component in particular, display superior forecasting performance regardless of whether the idiosyncratic volatility estimates are generated at the local (Saudi Arabian) level or the regional (Gulf Cooperation Council [GCC]) level. The results are of particular interest to investors that are not mean variance optimizers.