Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/103633
Authors: 
Caporin, Massimiliano
McAleer, Michael
Year of Publication: 
2013
Citation: 
[Journal:] Econometrics [ISSN:] 2225-1146 [Publisher:] MDPI [Place:] Basel [Volume:] 1 [Year:] 2013 [Issue:] 1 [Pages:] 115-126
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 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.
Subjects: 
DCC representation
BEKK
GARCC
stated representation
derived model
conditional correlations
two step estimators
assumed asymptotic properties
filter
JEL: 
C18
C32
C58
G17
Persistent Identifier of the first edition: 
Creative Commons License: 
http://creativecommons.org/licenses/by/3.0/
Document Type: 
Article
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