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Econometrics 2013, 1(1), 115-126; doi:10.3390/econometrics1010115
Article

Ten Things You Should Know about the Dynamic Conditional Correlation Representation

1
 and
2,3,4,*
1 Department of Economics and Management "Marco Fanno", University of Padova, Via del Santo 33, 35123 Padova, Italy 2 Econometric Institute, Erasmus School of Economics, Erasmus University Rotterdam and Tinbergen Institute, 3000 DR Rotterdam, Netherlands 3 Department of Quantitative Economics, Complutense University of Madrid, 28223 Pozuelo de Alarcón, Madrid, Spain 4 Institute of Economic Research, Kyoto University, Kyoto 606-8501, Japan
* Author to whom correspondence should be addressed.
Received: 13 May 2013 / Revised: 7 June 2013 / Accepted: 14 June 2013 / Published: 21 June 2013
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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 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 Generalized Autoregressive Conditional Correlation (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 Baba, Engle, Kraft and Kroner (BEKK) in small systems; and DCC may be a useful filter or a diagnostic check, but it is not a model.
Keywords: DCC representation; BEKK; GARCC; stated representation; derived model; conditional correlations; two step estimators; assumed asymptotic properties; filter DCC representation; BEKK; GARCC; stated representation; derived model; conditional correlations; two step estimators; assumed asymptotic properties; filter
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Caporin, M.; McAleer, M. Ten Things You Should Know about the Dynamic Conditional Correlation Representation. Econometrics 2013, 1, 115-126.

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