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Do We Really Need Both BEKK and DCC? A Tale of Two Covariance Models



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Caporin, Massimiliano y McAleer, Michael (2009) Do We Really Need Both BEKK and DCC? A Tale of Two Covariance Models. [ Documentos de trabajo del Instituto Complutense de Análisis Económico (ICAE); nº 04, 2009, ] (No publicado)

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URL Oficial: http://eprints.ucm.es/8590/

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Large and very large portfolios of financial assets are routine for many individuals and organizations. The two most widely used models of conditional covariances and correlations are BEKK and DCC. BEKK suffers from the archetypal "curse of dimensionality" whereas DCC does not. This is a misleading interpretation of the suitability of the two models to be used in practice. The primary purposes of the paper are to define targeting as an aid in estimating matrices associated with large numbers of financial assets, analyze the similarities and dissimilarities between BEKK and DCC, both with and without targeting, on the basis of structural derivation, the analytical forms of the sufficient conditions for the existence of moments, and the sufficient conditions for consistency and asymptotic normality, and computational tractability for very large (that is, ultra high) numbers of financial assets, to present a consistent two step estimation method for the DCC model, and to determine whether BEKK or DCC should be preferred in practical applications.

Tipo de documento:Documento de trabajo o Informe técnico
Información Adicional:

Massimiliano Caporin pertenece al Dipartimento di Scienze Economiche “Marco Fanno”, Università degli Studi di Padova,Facoltà di Scienze Statistiche.

Palabras clave:Conditional correlations, Conditional covariances, Diagonal models, Forecasting, Generalized models, Hadamard models, Scalar models, Targeting.
Materias:Ciencias > Estadística > Estadística Matemática
JEL:G11, G33, C32
Título de serie o colección:Documentos de trabajo del Instituto Complutense de Análisis Económico (ICAE)
Código ID:8590
Depositado:03 Mar 2009 10:11
Última Modificación:07 Jun 2017 11:52

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