# 13-078/III (2013-06-10)

Author(s)
Massimiliano Caporin, University of Padova, Italy; Michael McAleer, Erasmus University Rotterdam, The Netherlands, Complutense University of Madrid, Spain, and Kyoto University, Japan
Keywords:
DCC representation, BEKK, GARCC, stated representation, derived model, conditional covariances, conditional correlations, regularity conditions, moments, two step estimators, assumed properties, asymptotic properties, filter, diagnostic check
JEL codes:
C18, C32, C58, G17

See the publication in Econometrics (2013). Volume 1(1), pages 115-126.

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.