Bayesian analysis of ARMA-GARCH models: A Markov chain sampling approach

Teruo Nakatsuma

doi:10.1016/S0304-4076(99)00029-9

Bayesian analysis of ARMA-GARCH models: A Markov chain sampling approach

Teruo Nakatsuma

Research output: Contribution to journal › Article › peer-review

77 Citations (Scopus)

Abstract

We develop a Markov chain Monte Carlo method for a linear regression model with an ARMA(p, q)-GARCH(r, s) error. To generate a Monte Carlo sample from the joint posterior distribution, we employ a Markov chain sampling with the Metropolis-Hastings algorithm. As illustration, we estimate an ARMA-GARCH model of simulated time series data.

Original language	English
Pages (from-to)	57-69
Number of pages	13
Journal	Journal of Econometrics
Volume	95
Issue number	1
DOIs	https://doi.org/10.1016/S0304-4076(99)00029-9
Publication status	Published - 2000 Mar
Externally published	Yes

Keywords

ARMA process
Bayesian inference
GARCH
Markov chain Monte Carlo
Metropolis-Hastings algorithm

ASJC Scopus subject areas

Economics and Econometrics

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10.1016/S0304-4076(99)00029-9

Cite this

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title = "Bayesian analysis of ARMA-GARCH models: A Markov chain sampling approach",

abstract = "We develop a Markov chain Monte Carlo method for a linear regression model with an ARMA(p, q)-GARCH(r, s) error. To generate a Monte Carlo sample from the joint posterior distribution, we employ a Markov chain sampling with the Metropolis-Hastings algorithm. As illustration, we estimate an ARMA-GARCH model of simulated time series data.",

keywords = "ARMA process, Bayesian inference, GARCH, Markov chain Monte Carlo, Metropolis-Hastings algorithm",

author = "Teruo Nakatsuma",

year = "2000",

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doi = "10.1016/S0304-4076(99)00029-9",

language = "English",

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T1 - Bayesian analysis of ARMA-GARCH models

T2 - A Markov chain sampling approach

AU - Nakatsuma, Teruo

PY - 2000/3

Y1 - 2000/3

N2 - We develop a Markov chain Monte Carlo method for a linear regression model with an ARMA(p, q)-GARCH(r, s) error. To generate a Monte Carlo sample from the joint posterior distribution, we employ a Markov chain sampling with the Metropolis-Hastings algorithm. As illustration, we estimate an ARMA-GARCH model of simulated time series data.

AB - We develop a Markov chain Monte Carlo method for a linear regression model with an ARMA(p, q)-GARCH(r, s) error. To generate a Monte Carlo sample from the joint posterior distribution, we employ a Markov chain sampling with the Metropolis-Hastings algorithm. As illustration, we estimate an ARMA-GARCH model of simulated time series data.

KW - ARMA process

KW - Bayesian inference

KW - GARCH

KW - Markov chain Monte Carlo

KW - Metropolis-Hastings algorithm

UR - https://www.scopus.com/pages/publications/0347318519

UR - https://www.scopus.com/inward/citedby.url?scp=0347318519&partnerID=8YFLogxK

U2 - 10.1016/S0304-4076(99)00029-9

DO - 10.1016/S0304-4076(99)00029-9

M3 - Article

AN - SCOPUS:0347318519

SN - 0304-4076

VL - 95

SP - 57

EP - 69

JO - Journal of Econometrics

JF - Journal of Econometrics

IS - 1

ER -

Bayesian analysis of ARMA-GARCH models: A Markov chain sampling approach

Abstract

Keywords

ASJC Scopus subject areas

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