Robust ranking of multivariate GARCH models by problem dimension

During the last 15 years, several Multivariate GARCH (MGARCH) models have appeared in the literature. Recent research has begun to examine MGARCH specifications in terms of their out-of-sample forecasting performance. We provide an empirical comparison of alternative MGARCH models, namely BEKK, DCC, Corrected DCC (cDCC), CCC, OGARCH Exponentially Weighted Moving Average, and covariance shrinking, using historical data for 89 US equities. We contribute to the literature in several directions. First, we consider a wide range of models, including the recent cDCC and covariance shrinking models. Second, we use a range of tests and approaches for direct and indirect model comparison, including the Model Confidence Set. Third, we examine how the robust model rankings are influenced by the crosssectional dimension of the problem. 1 Department of Economics and Management “Marco Fanno”, University of Padova, Italy. 2 Econometric Institute, Erasmus School of Economics, Erasmus University, Rotterdam; Tinbergen Institute, The Netherlands; Department of Quantitative Economics, Complutense University of Madrid; and Institute of Economic Research, Kyoto University. *Corresponding Author: Università degli Studi di Padova, Dipartimento di Scienze Economiche ed Aziendali “Marco Fanno”, Via del Santo, 33, 35123 Padova, Italy, ph. +39-0498274259, fax. +39-0498274211. Email: [email protected].