Ten Things You Should Know About the Dynamic Conditional Correlation Representation
| Autor: | Caporin, M., McAleer, M.J. |
|---|---|
| Rok vydání: | 2013 |
| Předmět: |
jel:G17
BEKK DCC representation GARCC assumed properties asymptotic properties conditional correlations conditional covariances derived model diagnostic check filter financial econometrics moments regularity conditions stated representation two step estimators jel:C19 Nuclear Theory Computer Science::Programming Languages jel:C32 Nuclear Experiment jel:C59 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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