%0 Journal Article
%T Ten Things You Should Know about the Dynamic Conditional Correlation Representation
%A Massimiliano Caporin
%A Michael McAleer
%J Econometrics
%D 2013
%I MDPI AG
%R 10.3390/econometrics1010115
%X 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.
%K DCC representation
%K BEKK
%K GARCC
%K stated representation
%K derived model
%K conditional correlations
%K two step estimators
%K assumed asymptotic properties
%K filter
%U http://www.mdpi.com/2225-1146/1/1/115