Template-type: ReDIF-Paper 1.0
Author-Name: Manabu Asai
Author-Workplace-Name: Faculty of Economics Soka University, Japan.

Author-Name:
 Michael McAleer
Author-Workplace-Name:
 Department of Quantitative Finance National Tsing Hua University, Taiwan and Econometric Institute Erasmus School of 
	Economics Erasmus University Rotterdam, The Netherlands and Department of Quantitative Economics Complutense University of 
	Madrid, Spain And Institute of Advanced Sciences Yokohama National University, Japan.
Title: Bayesian analysis of realized matrix-exponential
 GARCH models
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.
Classification-JEL: C11, C32.
Keywords: Multivariate GARCH; Realized Measure; Matrix-Exponential; Bayesian Markov chain Monte Carlo method; Asymmetry.
Length: 28 pages 
Creation-Date: 2018-01
Number: 2018-04
X-File-Ref: http://america.sim.ucm.es/repec/ucm/ref/doicae1804.txt
File-URL: https://eprints.ucm.es/id/eprint/46148/1/1804.pdf
File-Format: Application/pdf
Handle: RePEc:ucm:doicae:1804