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Bayesian analysis of realized matrix-exponential GARCH models

Manabu Asai and Michael McAleer

No 2018-04, Documentos de Trabajo del ICAE from Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico

Abstract: 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.

Keywords: Multivariate GARCH; Realized Measure; Matrix-Exponential; Bayesian Markov chain Monte Carlo method; Asymmetry. (search for similar items in EconPapers)
JEL-codes: C11 C32 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ets
Date: 2018-01
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Related works:
Working Paper: Bayesian Analysis of Realized Matrix-Exponential GARCH Models (2018) Downloads
Working Paper: Bayesian Analysis of Realized Matrix-Exponential GARCH Models (2018) Downloads
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