Arbeitspapier
Bayesian Analysis of Realized Matrix-Exponential GARCH Models
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.
- Sprache
-
Englisch
- Erschienen in
-
Series: Tinbergen Institute Discussion Paper ; No. TI 2018-005/III
- Klassifikation
-
Wirtschaft
Bayesian Analysis: General
Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
- Thema
-
Multivariate
GARCH
Realized
Measures
Matrix-Exponential
Bayesian
Markov
Chain
Monte
Carlo
method
Asymmetry
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Asai, Manabu
McAleer, Michael
- Ereignis
-
Veröffentlichung
- (wer)
-
Tinbergen Institute
- (wo)
-
Amsterdam and Rotterdam
- (wann)
-
2018
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:45 MEZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Asai, Manabu
- McAleer, Michael
- Tinbergen Institute
Entstanden
- 2018
Ähnliche Objekte (12)
Realized Matrix-Exponential Stochastic Volatility with Asymmetry, Long Memory and Spillovers
Realized volatility: a review
Forecasting Realized Volatility with Linear and Nonlinear Models
Realized Volatility Risk
Asymptotic Theory for Extended Asymmetric Multivariate GARCH Processes
Asymmetric realized volatility risk
Asymmetric Realized Volatility Risk
Forecasting Co-Volatilities via Factor Models with Asymmetry and Long Memory in Realized Covariance
Realized Stochastic Volatility with General Asymmetry and Long Memory
Realized Stochastic Volatility Models with Generalized Gegenbauer Long Memory
Structure and asymptotic theory for nonlinear models with GARCH errors
Structure and asymptotic theory for STAR(1)-GARCH(1,1) models
Realized Matrix-Exponential Stochastic Volatility with Asymmetry, Long Memory and Spillovers
Realized volatility: a review
Forecasting Realized Volatility with Linear and Nonlinear Models
Realized Volatility Risk
Asymptotic Theory for Extended Asymmetric Multivariate GARCH Processes
Asymmetric realized volatility risk
Asymmetric Realized Volatility Risk
Forecasting Co-Volatilities via Factor Models with Asymmetry and Long Memory in Realized Covariance
Realized Stochastic Volatility with General Asymmetry and Long Memory
Realized Stochastic Volatility Models with Generalized Gegenbauer Long Memory
Structure and asymptotic theory for nonlinear models with GARCH errors
Structure and asymptotic theory for STAR(1)-GARCH(1,1) models
Realized Matrix-Exponential Stochastic Volatility with Asymmetry, Long Memory and Spillovers
Realized volatility: a review
Forecasting Realized Volatility with Linear and Nonlinear Models
Realized Volatility Risk
Asymptotic Theory for Extended Asymmetric Multivariate GARCH Processes
Asymmetric realized volatility risk
Asymmetric Realized Volatility Risk
Forecasting Co-Volatilities via Factor Models with Asymmetry and Long Memory in Realized Covariance
Realized Stochastic Volatility with General Asymmetry and Long Memory
Realized Stochastic Volatility Models with Generalized Gegenbauer Long Memory
Structure and asymptotic theory for nonlinear models with GARCH errors