Ten things you should know about the dynamic conditional correlation representation

• Published in: Econometrics Vol. 1; no. 1; pp. 115 - 126
• Main Authors: Caporin, Massimiliano, McAleer, Michael
• Format: Journal Article
• Language: English
• Published: Basel MDPI 01.06.2013; MDPI AG
• Subjects: assumed asymptotic properties; BEKK; conditional correlations; DCC representation; derived model; Econometrics; Economics; Estimating techniques; filter; GARCC; Korrelation; Standardization; stated representation; Statistische Methode; Studies; Theorie; two step estimators
• ISSN: 2225-1146; 2225-1146
• DOI: 10.3390/econometrics1010115

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.