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Robust Ranking of Multivariate GARCH Models by Problem Dimension


Caporin, Massimiliano y McAleer, Michael (2012) Robust Ranking of Multivariate GARCH Models by Problem Dimension. [ Documentos de Trabajo del Instituto Complutense de Análisis Económico (ICAE); nº 06, 2012, ] (No publicado)

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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.

Tipo de documento:Documento de trabajo o Informe técnico
Palabras clave:Covariance forecasting, Model confidence set, Robust model ranking, MGARCH, Robust model comparison.
Materias:Ciencias Sociales > Economía > Econometría
Título de serie o colección:Documentos de Trabajo del Instituto Complutense de Análisis Económico (ICAE)
Código ID:14821

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Última Modificación:06 Feb 2014 10:10

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