Theory and inference for a Markov switching GARCH model

Luc Bauwens, Arie Preminger and Jeroen Rombouts

No 2007055, LIDAM Discussion Papers CORE from Université catholique de Louvain, Center for Operations Research and Econometrics (CORE)

Abstract: 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 SP500 daily returns.

Keywords: GARCH; Markov-switching; Bayesian inference (search for similar items in EconPapers)
JEL-codes: C11 C22 C52 (search for similar items in EconPapers)
Date: 2007-08-01
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Citations: View citations in EconPapers (8)

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Related works:
Journal Article: Theory and inference for a Markov switching GARCH model (2010)
Working Paper: Theory and inference for a Markov switching Garch model (2010)
Working Paper: Theory and inference for a Markov switching GARCH model (2007)
Working Paper: Theory and inference for a Markov switching Garch model (2007)
Working Paper: Theory and Inference for a Markov-Switching GARCH Model (2007)
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