Time-Varying Realized GARCH Models for Tracking Measurement Error Bias in Volatility Forecasting

This paper proposes novel approaches to the modeling of attenuation bias effects in volatility forecasting. Our strategy relies on suitable generalizations of the Realized GARCH model by Hansen et al. (2012) where the impact of lagged realized measures on the current conditional variance is weighted according to the accuracy of the measure itself at that specific time point. This feature allows assigning more weight to lagged volatilities when they are more accurately measured. The merits of the proposed specifications are assessed by means of an application to the prediction of Value at Risk (VaR) and Expected Shortfall (ES) for a set of stock market indices. The results of the empirical analysis show that the proposed specifications are able to outperform standard Realized GARCH models in VaR and ES forecasting.