# 18-005/III (2018-01-17)

Manabu Asai, Soka University, Japan; Michael McAleer, Asia University, Taiwan; University of Sydney Business School, Australia; Erasmus School of Economics, Erasmus University Rotterdam, The Netherlands; Complutense University of Madrid, Spain; Yokohama National University, Japan
C11, C32
JEL codes:
Multivariate GARCH, Realized Measures, Matrix-Exponential, Bayesian Markov Chain Monte Carlo method, Asymmetry

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.