This paper proposes and tests a new framework for weighting recursive out-of-sample prediction errors in accordance with their corresponding in-sample estimation uncertainty. In essence, we show how as much information from the sample as possible can be used in the evaluation of prediction...

A large number of important practical tasks can be accomplished using a multivariate GARCH model. This paper examines the relatively small number of software packages that are currently available for estimating such models, in spite of their widespread use. The review focuses upon estimation...

We consider the finite sample properties of model selection by information criteria in conditionally heteroscedastic models. Recent theoretical results show that certain popular criteria are consistent in that they will select the true model asymptotically with probability 1. To examine the...