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Robust Ranking of Multivariate GARCH Models by Problem Dimension

Author

Listed:
  • Michael McAleer

    (Erasmus University Rotterdam,Tinbergen Institute,Kyoto University,Complutense University of Madrid)

  • Massimiliano Caporin

    (Department of Economics and Management“Marco Fanno†University of Padova,Italy)

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 cross- sectional dimension of the problem.

Suggested Citation

  • Michael McAleer & Massimiliano Caporin, 2012. "Robust Ranking of Multivariate GARCH Models by Problem Dimension," KIER Working Papers 815, Kyoto University, Institute of Economic Research.
    • Handle: RePEc:kyo:wpaper:815
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    More about this item

    Keywords

    Covariance forecasting; model confidence set; robust model ranking; MGARCH; robust model comparison.;
    All these keywords.

    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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