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Erscheinungsjahr: 
2013
Quellenangabe: 
[Journal:] Econometrics [ISSN:] 2225-1146 [Volume:] 1 [Issue:] 1 [Publisher:] MDPI [Place:] Basel [Year:] 2013 [Pages:] 115-126
Verlag: 
MDPI, Basel
Zusammenfassung: 
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.
Schlagwörter: 
DCC representation
BEKK
GARCC
stated representation
derived model
conditional correlations
two step estimators
assumed asymptotic properties
filter
JEL: 
C18
C32
C58
G17
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