Asymptotic Theory for Extended Asymmetric Multivariate GARCH Processes
The paper considers various extended asymmetric multivariate conditional volatility models, and derives appropriate regularity conditions and associated asymptotic theory. This enables checking of internal consistency and allows valid statistical inferences to be drawn based on empirical estimation....
| Autores: | , |
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| Tipo de recurso: | informe técnico |
| Fecha de publicación: | 2016 |
| País: | España |
| Institución: | Universidad Complutense de Madrid (UCM) |
| Repositorio: | Docta Complutense |
| Idioma: | inglés |
| OAI Identifier: | oai:docta.ucm.es:20.500.14352/27597 |
| Acceso en línea: | https://hdl.handle.net/20.500.14352/27597 |
| Access Level: | acceso abierto |
| Palabra clave: | C13 C32 C58 Multivariate conditional volatility Vector random coefficient autoregressive process Asymmetry Long memory Dynamic conditional correlations Regularity conditions Asymptotic properties. Econometría (Economía) 5302 Econometría |
| Sumario: | The paper considers various extended asymmetric multivariate conditional volatility models, and derives appropriate regularity conditions and associated asymptotic theory. This enables checking of internal consistency and allows valid statistical inferences to be drawn based on empirical estimation. For this purpose, we use an underlying vector random coefficient autoregressive process, for which we show the equivalent representation for the asymmetric multivariate conditional volatility model, to derive asymptotic theory for the quasi-maximum likelihood estimator. As an extension, we develop a new multivariate asymmetric long memory volatility model, and discuss the associated asymptotic properties. |
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