Theory and inference for a Markov switching GARCH model

Luc Bauwens; Arie Preminger; Jeroen V.K. Rombouts

doi:10.1111/j.1368-423X.2009.00307.x

Theory and inference for a Markov switching GARCH model

Luc Bauwens
, Arie Preminger
, Jeroen V.K. Rombouts

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

125 Scopus citations

Abstract

Summary: We develop a Markov-switching GARCH model (MS-GARCH) wherein the conditional mean and variance switch in time from one GARCH process to another. The switching is governed by a hidden Markov chain. We provide sufficient conditions for geometric ergodicity and existence of moments of the process. Because of path dependence, maximum likelihood estimation is not feasible. By enlarging the parameter space to include the state variables, Bayesian estimation using a Gibbs sampling algorithm is feasible. We illustrate the model on S&P500 daily returns.

Original language	English
Pages (from-to)	218-244
Number of pages	27
Journal	Econometrics Journal
Volume	13
Issue number	2
DOIs	https://doi.org/10.1111/j.1368-423X.2009.00307.x
State	Published - 1 Jan 2010
Externally published	Yes

Keywords

Bayesian inference
GARCH
Markov-switching

ASJC Scopus subject areas

Economics and Econometrics

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10.1111/j.1368-423X.2009.00307.x

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abstract = "Summary: We develop a Markov-switching GARCH model (MS-GARCH) wherein the conditional mean and variance switch in time from one GARCH process to another. The switching is governed by a hidden Markov chain. We provide sufficient conditions for geometric ergodicity and existence of moments of the process. Because of path dependence, maximum likelihood estimation is not feasible. By enlarging the parameter space to include the state variables, Bayesian estimation using a Gibbs sampling algorithm is feasible. We illustrate the model on S\&P500 daily returns.",

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AU - Preminger, Arie

AU - Rombouts, Jeroen V.K.

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N2 - Summary: We develop a Markov-switching GARCH model (MS-GARCH) wherein the conditional mean and variance switch in time from one GARCH process to another. The switching is governed by a hidden Markov chain. We provide sufficient conditions for geometric ergodicity and existence of moments of the process. Because of path dependence, maximum likelihood estimation is not feasible. By enlarging the parameter space to include the state variables, Bayesian estimation using a Gibbs sampling algorithm is feasible. We illustrate the model on S&P500 daily returns.

AB - Summary: We develop a Markov-switching GARCH model (MS-GARCH) wherein the conditional mean and variance switch in time from one GARCH process to another. The switching is governed by a hidden Markov chain. We provide sufficient conditions for geometric ergodicity and existence of moments of the process. Because of path dependence, maximum likelihood estimation is not feasible. By enlarging the parameter space to include the state variables, Bayesian estimation using a Gibbs sampling algorithm is feasible. We illustrate the model on S&P500 daily returns.

KW - Bayesian inference

KW - GARCH

KW - Markov-switching

UR - https://www.scopus.com/pages/publications/77953777033

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DO - 10.1111/j.1368-423X.2009.00307.x

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EP - 244

JO - Econometrics Journal

JF - Econometrics Journal

IS - 2

ER -

Theory and inference for a Markov switching GARCH model

Abstract

Keywords

ASJC Scopus subject areas

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