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Ten Things You Should Know About the Dynamic Conditional Correlation Representation

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  • Caporin, M.
  • McAleer, M.J.

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 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.

Suggested Citation

  • Caporin, M. & McAleer, M.J., 2013. "Ten Things You Should Know About the Dynamic Conditional Correlation Representation," Econometric Institute Research Papers EI 2013-21, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    • Handle: RePEc:ems:eureir:40377
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    References listed on IDEAS

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    More about this item

    Keywords

    BEKK; DCC representation; GARCC; assumed properties; asymptotic properties; conditional correlations; conditional covariances; derived model; diagnostic check; filter; financial econometrics; moments; regularity conditions; stated representation; two step estimators;
    All these keywords.

    JEL classification:

    • C19 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Other
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C59 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Other
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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