Bayesian Analysis of Realized Matrix-Exponential GARCH Models
Manabu Asai and
No 2018-005/III, Econometric Institute Research Papers from Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute
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 nancial 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)
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Working Paper: Bayesian Analysis of Realized Matrix-Exponential GARCH Models (2018)
Working Paper: Bayesian analysis of realized matrix-exponential GARCH models (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:ems:eureir:104259
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