Template-Type: ReDIF-Paper 1.0
Author-Name: Asai, M.
Author-Name-Last: Asai
Author-Name-First: Manabu
Author-Person: pas73
Author-Name: McAleer, M.J.
Author-Name-Last: McAleer
Author-Name-First: Michael
Author-Person: pmc90
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
 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.
Length: 27
Creation-Date: 2018-01-01
File-URL: https://repub.eur.nl/pub/104259/EI2018-07.pdf
File-Format: application/pdf
Series: RePEc:ems:eureir
Number: 2018-005/III
Number: EI 2018-07
Classification-JEL: C11, C32
Keywords: Multivariate GARCH, Realized Measure, Matrix-Exponential, Bayesian Markov, chain Monte Carlo method, Asymmetry
Handle: RePEc:ems:eureir:104259