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    Robust Ranking of Multivariate GARCH Models by Problem Dimension

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    Author
    Caporin, M.
    McAleer, M.
    Date
    2012
    Permanent Link
    http://hdl.handle.net/10092/9783

    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.

    Subjects
    Covariance forecasting
     
    model confidence set
     
    robust model ranking
     
    MGARCH
     
    robust model comparison
     
    Field of Research::14 - Economics::1401 - Economic Theory::140103 - Mathematical Economics
     
    Field of Research::14 - Economics::1402 - Applied Economics::140207 - Financial Economics
    Collections
    • Business and Law: Working Papers [180]
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