Caporin, MassimilianoMcAleer, Michael2023-06-192023-06-192013-06https://hdl.handle.net/20.500.14352/41489Revised: June 2013 The authors most are grateful to two referees for very helpful comments and suggestions. For financial support, the second author wishes to acknowledge the Australian Research Council, National Science Council, Taiwan, and the Japan Society for the Promotion of Science. An earlier version of this paper was distributed as “Ten Things You Should Know About DCC”.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.engAtribución-NoComercial 3.0 Españahttps://creativecommons.org/licenses/by-nc/3.0/es/technical reporthttps://www.ucm.es/icaeopen accessC18C32C58G17DCC representationBEKKGARCCStated representationDerived modelConditional covariancesConditional correlationsregularity conditionsmomentstwo step estimatorsAssumed propertiesAsymptotic propertiesfilterDiagnostic check.Econometría (Economía)5302 Econometría