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Robust Ranking of Multivariate GARCH Models by Problem Dimension

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Abstract

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.

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  • Massimiliano Caporin & Michael McAleer, 2012. "Robust Ranking of Multivariate GARCH Models by Problem Dimension," Working Papers in Economics 12/06, University of Canterbury, Department of Economics and Finance.
    • Handle: RePEc:cbt:econwp:12/06
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    JEL classification:

    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • Y10 - Miscellaneous Categories - - Data: Tables and Charts - - - Data: Tables and Charts

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