Ten Things You Should Know About the Dynamic Conditional Correlation Representation

By Massimiliano Caporin, Michael Mcaleer. 2013

117 citations recorded by the source corpus.

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.

Subfields: Economics and Econometrics, Finance, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/bfa5597311b92591ff8e6eee5d701099297a73a2

Citations in the corpus

• An empirical investigation of volatility dynamics in the cryptocurrency market (2019, unknown): “…2012; Boldanov et al., 2016), due to the fact that consistency and asymptotic normality of the estimated parameters of any version of the DCC model have not yet been derived (Caporin and McAleer, 2012; 2013), despite the fact that BEKK and DCC models can be applied for similar investigations2.”