We investigate the predictive performance of various classes of value-at-risk (VaR) models in several dimensions — unfiltered versus filtered VaR models, parametric versus nonparametric distributions, conventional versus extreme value distributions, and quantile regression versus inverting the...

In this paper we discuss how to compare various (possibly misspecified) density forecast models using the Kullback-Leibler information criterion (KLIC) of a candidate density forecast model with respect to the true density. The KLIC differential between a pair of competing models is the...

We study the properties of the multi-period-ahead least-squares forecast for the stationary AR(1) model under a general error distribution. We find that the forecast is unbiased up to O(T^(−1)), where T is the in-sample size, regardless of the error distribution and that the mean squared...