Bayesian Analysis of Realized Matrix-Exponential GARCH Models
Tinbergen Institute Discussion Paper 2018-005/III
29 Pages Posted: 25 Jan 2018
Date Written: January 17, 2018
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 Measures, Matrix-Exponential, Bayesian Markov Chain Monte Carlo method, Asymmetry
JEL Classification: C11, C32
Suggested Citation: Suggested Citation