Ten Things You Should Know About DCC
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: D...
| Autores: | , |
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| Tipo de recurso: | informe técnico |
| Fecha de publicación: | 2013 |
| País: | España |
| Institución: | Universidad Complutense de Madrid (UCM) |
| Repositorio: | Docta Complutense |
| Idioma: | inglés |
| OAI Identifier: | oai:docta.ucm.es:20.500.14352/41466 |
| Acceso en línea: | https://hdl.handle.net/20.500.14352/41466 |
| Access Level: | acceso abierto |
| Palabra clave: | C18 C32 C58 G17 DCC BEKK GARCC Stated representation Derived model Conditional covariances Conditional correlations Regularity conditions Moments Two step estimators Assumed properties Asymptotic properties Filter Diagnostic check. Econometría (Economía) 5302 Econometría |
| Sumario: | 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. |
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