Artikel
Ten things you should know about the dynamic conditional correlation representation
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.
- Sprache
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Englisch
- Erschienen in
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Journal: Econometrics ; ISSN: 2225-1146 ; Volume: 1 ; Year: 2013 ; Issue: 1 ; Pages: 115-126 ; Basel: MDPI
- Klassifikation
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Wirtschaft
Methodological Issues: General
Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
Financial Econometrics
Financial Forecasting and Simulation
- Thema
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DCC representation
BEKK
GARCC
stated representation
derived model
conditional correlations
two step estimators
assumed asymptotic properties
filter
Korrelation
Statistische Methode
Theorie
- Ereignis
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Geistige Schöpfung
- (wer)
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Caporin, Massimiliano
McAleer, Michael
- Ereignis
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Veröffentlichung
- (wer)
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MDPI
- (wo)
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Basel
- (wann)
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2013
- DOI
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doi:10.3390/econometrics1010115
- Handle
- Letzte Aktualisierung
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10.03.2025, 11:45 MEZ
Datenpartner
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Objekttyp
- Artikel
Beteiligte
- Caporin, Massimiliano
- McAleer, Michael
- MDPI
Entstanden
- 2013