Robust ranking of multivariate GARCH models by problem dimension

Massimiliano Caporin and Michael McAleer

Computational Statistics & Data Analysis, 2014, vol. 76, issue C, 172-185

Abstract: Several Multivariate GARCH (MGARCH) models have been proposed, and recently such MGARCH specifications have been examined in terms of their out-of-sample forecasting performance. An empirical comparison of alternative MGARCH models is provided, which focuses on the BEKK, DCC, Corrected DCC (cDCC), CCC, OGARCH models, Exponentially Weighted Moving Average, and covariance shrinking, all fitted to historical data for 89 US equities. Notably, a wide range of models, including the recent cDCC model and the covariance shrinking method, are used. Several tests and approaches for direct and indirect model comparison, including the Model Confidence Set, are considered. Furthermore, the robustness of model rankings to the cross-sectional dimension of the problem is analyzed.

Keywords: Covariance forecasting; Model confidence set; Robust model ranking; MGARCH; Robust model comparison (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (24)

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Related works:
Working Paper: Robust Ranking of Multivariate GARCH Models by Problem Dimension (2012)
Working Paper: Robust Ranking of Multivariate GARCH Models by Problem Dimension (2012)
Working Paper: Robust Ranking of Multivariate GARCH Models by Problem Dimension (2012)
Working Paper: Robust Ranking of Multivariate GARCH Models by Problem Dimension (2012)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:76:y:2014:i:c:p:172-185

DOI: 10.1016/j.csda.2012.05.012

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