Volatility forecasts: a continuous time model versus discrete time models

Abstract

This paper compares empirically the forecasting performance of a continuous time stochastic volatility model with two volatility factors (SV2F) to a set of alternative models (GARCH, FIGARCH, HYGARCH, FIEGARCH and Component GARCH). We use two loss functions and two out-of-sample periods in the forecasting evaluation. The two out-of-sample periods are characterized by different patterns of volatility. The volatility is rather low and constant over the first period but shows a significant increase over the second out-of-sample period. The empirical results evidence that the performance of the alternative models depends on the characteristics of the out-ofsample periods and on the forecasting horizons. Contrarily, the SV2F forecasting performance seems to be unaffected by these two facts, since the model provides the most accurate volatility forecasts according to the loss functions we consider.