Ten Things You Should Know about the Dynamic Conditional Correlation Representation

Econometrics

Massimiliano Caporin¹ 

[1] Department of Economics and Management “Marco Fanno”, University of Padova, Via del Santo 33, 35123 Padova, Italy; E-Mail:

关键词: DCC representation;    BEKK;    GARCC;    stated representation;    derived model;    conditional correlations;    two step estimators;    assumed asymptotic properties;    filter;   

DOI  :  10.3390/econometrics1010115

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2013 by the authors; licensee MDPI, Basel, Switzerland.