Model selection for volatility prediction

Abstract

We consider a stochastic regression model defined by stochastic differential equations. Based on an expected Kullback-Leibler information for the approximated distributions, we propose an information criterion for selection of volatility models. We show that the information criterion is asymptotically unbiased for the expectedKullback-Leibler information.We also give examples and simulation results of model selection.