Bayesian analysis of realized matrix-exponential GARCH models
The paper develops a new realized matrix-exponential GARCH (MEGARCH) model, which uses the information of returns and realized measure of co-volatility matrix simultaneously. The paper also considers an alternative multivariate asymmetric function to develop news impact curves. We consider Bayesian...
| Autores: | , |
|---|---|
| Tipo de recurso: | informe técnico |
| Fecha de publicación: | 2018 |
| País: | España |
| Institución: | Universidad Complutense de Madrid (UCM) |
| Repositorio: | Docta Complutense |
| Idioma: | inglés |
| OAI Identifier: | oai:docta.ucm.es:20.500.14352/17409 |
| Acceso en línea: | https://hdl.handle.net/20.500.14352/17409 |
| Access Level: | acceso abierto |
| Palabra clave: | C11 C32 Multivariate GARCH Realized Measure Matrix-Exponential Bayesian Markov chain Monte Carlo method Asymmetry. Análisis matemático Econometría (Economía) 1202 Análisis y Análisis Funcional 5302 Econometría |
| Sumario: | The paper develops a new realized matrix-exponential GARCH (MEGARCH) model, which uses the information of returns and realized measure of co-volatility matrix simultaneously. The paper also considers an alternative multivariate asymmetric function to develop news impact curves. We consider Bayesian MCMC estimation to allow non-normal posterior distributions. For three US financial assets, we compare the realized MEGARCH models with existing multivariate GARCH class models. The empirical results indicate that the realized MEGARCH models outperform the other models regarding in-sample and out-of-sample performance. The news impact curves based on the posterior densities provide reasonable results. |
|---|