The likelihood of multivariate GARCH models is ill-conditioned
The likelihood of multivariate GARCH models is ill-conditioned because of two facts. First, financial time series often display high correlations, implying that an eigenvalue af the conditional covariances fluctuates near the zero boundary. Second, GARCH models explain conditional covariances in ter...
| Autores: | , , |
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| Tipo de recurso: | informe técnico |
| Fecha de publicación: | 1999 |
| País: | España |
| Institución: | Universidad Complutense de Madrid (UCM) |
| Repositorio: | Docta Complutense |
| Idioma: | inglés |
| OAI Identifier: | oai:docta.ucm.es:20.500.14352/64224 |
| Acceso en línea: | https://hdl.handle.net/20.500.14352/64224 |
| Access Level: | acceso abierto |
| Palabra clave: | ARCH GARCH Maximum-likelihood. Análisis Multivariante 1209.09 Análisis Multivariante |
| Sumario: | The likelihood of multivariate GARCH models is ill-conditioned because of two facts. First, financial time series often display high correlations, implying that an eigenvalue af the conditional covariances fluctuates near the zero boundary. Second, GARCH models explain conditional covariances in terms of a linear combination of delayed squared errors and their conditional expectation; this functional form implies that the likelihood function is almost flat in the neighborhood of the optimal estimates. Building on this analysis we propose a linear transformation of data which, not only stabilizes the likelihood computation, but also provides insight about the statistical properties of data. The use of this transfonnation is illustrated by modeling the short-run conditional correlations of four nominal exchange rates. |
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