Bayesian Analysis of Realized Matrix-Exponential GARCH Models

Manabu Asai and Michael McAleer

Computational Economics, 2022, vol. 59, issue 1, No 6, 103-123

Abstract: Abstract This study develops a new realized matrix-exponential GARCH (MEGARCH) model, which uses the information of returns and realized measure of co-volatility matrix simultaneously. An alternative multivariate asymmetric function to develop news impact curves is also considered. We consider Bayesian Markov chain Monte Carlo estimation to allow non-normal posterior distributions and illustrate the usefulness of the algorithm with numerical simulations for two assets. We compare the realized MEGARCH models with existing multivariate GARCH class models for three US financial assets . The empirical results indicate that the realized MEGARCH models outperform the other models regarding 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)
Date: 2022
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Citations: View citations in EconPapers (1)

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Related works:
Working Paper: Bayesian Analysis of Realized Matrix-Exponential GARCH Models (2018)
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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DOI: 10.1007/s10614-020-10074-6

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