Forecasting (LOG) Volatility Models

A number of volatility forecasting studies have led to the perception that the ARCH- and Stochastic Volatility-type models provide poor out-of-sample forecasts of volatility. This is primarily based on the use of traditional forecast evaluation criteria concerning the accuracy and the unbiasedness of forecasts. In this paper we provide an assessment of volatility forecasting. We use the Log- Volatility framework to show how the inherent noise in the approximation of the actual- and unobservable - volatility by the squared return results in a misleading forecast evaluation. We argue that evaluation problems are likely to be exacebated by non-normality of the shocks and that non-linear and utility-based criteria can be more suitable for the evaluation of volatility forecasts.