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.

DCC, GARCC, assumed properties, asymptotic properties, conditional correlations, conditional covariances, derived model, diagnostic check, filter, moments, regularity conditions, stated representation, two step estimators
Econometric and Statistical Methods: Other (jel C19), Time-Series Models; Dynamic Quantile Regressions (jel C32), Econometric Modeling: General (jel C50), Financial Forecasting (jel G17)
Tinbergen Institute
Tinbergen Institute Discussion Paper Series
Discussion paper / Tinbergen Institute
Erasmus School of Economics

Caporin, M, & McAleer, M.J. (2013). Ten Things you should know about DCC (No. TI 13-048/III). Discussion paper / Tinbergen Institute (pp. 1–20). Tinbergen Institute. Retrieved from