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.

BEKK, DCC, GARCC, assumed properties, asymptotic properties, conditional correlations, conditional covariances, derived model, diagnostic check, filter, moments, regularity conditions, stated representation, two step estimators
Erasmus School of Economics
hdl.handle.net/1765/39599
Econometric Institute Research Papers
Report / Econometric Institute, Erasmus University Rotterdam
Erasmus School of Economics

Caporin, M, & McAleer, M.J. (2013). Ten Things You Should Know About DCC (No. EI 2013-13). Report / Econometric Institute, Erasmus University Rotterdam (pp. 1–18). Erasmus School of Economics. Retrieved from http://hdl.handle.net/1765/39599