Modelling Short-Term Volatility with GARCH and HARCH Models

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Abstract
In this paper we present both a new formulation of the HARCH process and a study of the forecasting accuracy of ARCH-type models for predicting short-term volatility. Using high frequency data, the market volatility is expressed in terms of partial volatilities which are formally exponential moving averages of squared returns measured at different frequencies. This new formulation is shown to produce more accurate fits to the data and, at the same time, to be easier to compute than the earlier version of the HARCH process. This is obtained without losing the nice property of the HARCH process to identify different market components. In a second part, some performance measures of forecasting accuracy are discussed and the ARCH-type models are shown to be good predictors of the short-term hourly historical volatility with the new formulation of the HARCH process being the best predictor.