Ten Things You Should Know About the Dynamic Conditional Correlation Representation

Type of content
Discussion / Working Papers
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University of Canterbury. Department of Economics and Finance
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Caporin, M.
McAleer, M.

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 fore-casting 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 stand-ardized 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 prop-erties; 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.

Caporin, M., McAleer, M. (2013) Ten Things You Should Know About the Dynamic Conditional Correlation Representation. Department of Economics and Finance College of Business and Economics University of Canterbury..
DCC representation, BEKK, GARCC, stated representation, derived model, condi-tional covariances, conditional correlations, regularity conditions, moments, two step estimators, assumed properties, asymptotic properties, filter, diagnostic check
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ANZSRC fields of research
Field of Research::14 - Economics