A Model Confidence Set approach to the combination of multivariate volatility forecasts

In predicting conditional covariance matrices of financial portfolios, practitioners are required to choose among several alternative options, facing a number of different sources of uncertainty. A first source is related to the frequency at which prices are observed, either daily or intradaily. Using prices sampled at higher frequency inevitably poses additional sources of uncertainty related to the selection of the optimal intradaily sampling frequency and to the construction of the best realized estimator. Likewise, the choices of model structure and estimation method also have a critical role. In order to alleviate the impact of these sources of uncertainty, we propose a forecast combination strategy based on the Model Confidence Set [MCS] to adaptively identify the set of most accurate predictors. The combined predictor is shown to achieve superior performance with respect to the whole model universe plus three additional competitors, independently of the MCS or portfolio settings.