2026-06-27T15:22:17Zhttps://docta.ucm.es/rest/oai/requestoai:docta.ucm.es:20.500.14352/490092025-05-16T23:45:16Zcom_20.500.14352_14col_20.500.14352_15
Docta Complutense
author
Caporin, Massimiliano
author
McAleer, Michael
2023-06-20T09:13:15Z
2023-06-20T09:13:15Z
2011
Caporin, Massimiliano and McAleer, Michael, Ranking Multivariate GARCH Models by Problem Dimension (May 28, 2010). Available at SSRN: https://ssrn.com/abstract=1601236 or http://dx.doi.org/10.2139/ssrn.1601236
https://hdl.handle.net/20.500.14352/49009
https://dx.doi.org/10.2139/ssrn.1601236
https://www.ucm.es/icae
In 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. In this paper, we provide an empirical comparison of a set of models, namely BEKK, DCC, Corrected DCC (cDCC) of Aeilli (2008), CCC, Exponentially Weighted Moving Average, and covariance shrinking, using historical data of 89 US equities. Our methods follow part of the approach described in Patton and Sheppard (2009), and the paper contributes to the literature in several directions. First, we consider a wide range of models, including the recent cDCC model and covariance shrinking. Second, we use a range of tests and approaches for direct and indirect model comparison, including the Weighted Likelihood Ratio test of Amisano and Giacomini (2007). Third, we examine how the model rankings are influenced by the cross-sectional dimension of the problem.
eng
Atribución-NoComercial 3.0 España
Ranking Multivariate GARCH Models by Problem Dimension: An Empirical Evaluation
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