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Título:
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Ten Things You Should Know About the Dynamic Conditional Correlation Representation
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Autores:
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Caporin, Massimiliano ;
McAleer, Michael
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Tipo de documento:
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texto impreso
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Fecha de publicación:
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2013-06
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Dimensiones:
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application/pdf
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Nota general:
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cc_by_nc
info:eu-repo/semantics/openAccess
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Idiomas:
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Palabras clave:
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Estado = No publicado
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Materia = Ciencias Sociales: Economía: Econometría
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Tipo = Documento de trabajo o Informe técnico
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Resumen:
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The purpose of the paper is to discuss ten things potential users should know about the limits of the Dynamic Conditional Correlation (DCC) representation for estimating and forecasting time-varying conditional correlations. The reasons given for caution about the use of DCC include the following: DCC represents the dynamic conditional covariances of the standardized residuals, and hence does not yield dynamic conditional correlations; DCC is stated rather than derived; DCC has no moments; DCC does not have testable regularity conditions; DCC yields inconsistent two step estimators; DCC has no asymptotic properties; DCC is not a special case of GARCC, which has testable regularity conditions and standard asymptotic properties; DCC is not dynamic empirically as the effect of news is typically extremely small; DCC cannot be distinguished empirically from diagonal BEKK in small systems; and DCC may be a useful filter or a diagnostic check, but it is not a model.
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En línea:
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https://eprints.ucm.es/id/eprint/22109/1/1321.pdf
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