Template-Type: ReDIF-Paper 1.0
Series: Tinbergen Institute Discussion Papers
Creation-Date: 2013-03-21
Number: 13-048/III
Author-Name: Massimiliano Caporin
Author-Workplace-Name: University of Padova
Author-Name: Michael McAleer
Author-Workplace-Name: Erasmus University Rotterdam, University of Madrid, Kyoto University
Title: Ten Things you should know about DCC
Abstract: 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.
Classification-JEL: C18, C32, C58, G17
Keywords: DCC, BEKK, GARCC, Stated representation, Derived model, Conditional covariances, Conditional correlations, Regularity conditions, Moments, Two step estimators, Assumed properties, Asymptotic properties, Filter, Diagnostic check
File-Url: https://papers.tinbergen.nl/13048.pdf
File-Format: application/pdf
File-Size: 141536 bytes
Handle: RePEc:tin:wpaper:20130048