Ten Things You Should Know About the Dynamic Conditional Correlation Representation (2013)

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Type of Content
Discussion / Working PapersPublisher
University of Canterbury. Department of Economics and FinanceCollections
- Business: Working Papers [193]
- Working Papers in Economics [142]
Abstract
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.
Citation
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..This citation is automatically generated and may be unreliable. Use as a guide only.
Keywords
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 checkANZSRC Fields of Research
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