Forecasting Financial Time Series: Normal GARCH with Outliers or Heavy Tailed Distribution Assumptions?

The use of GARCH models is widely used as an effective method for capturing the volatility clustering inherent in financial returns series. The residuals from such models are however often non-Gaussian, and two methods suggest themselves for dealing with this; outlier removal, or use of non-Gaussian innovation distributions. While there are benefits to both, we show that the latter method is better if interest centers on volatility and value-at-risk prediction. New volatility measures based on OHLC (open high low close) data are derived and used. Use of OHLC measures are shown to be superior to use of the naive estimator used in other GARCH outlier studies.