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Theory and inference for a Markov switching GARCH model

Luc Bauwens (), Arie Preminger and Jeroen Rombouts ()

Econometrics Journal, 2010, vol. 13, issue 2, 218-244

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 S&P500 daily returns. Copyright The Author(s). Journal compilation Royal Economic Society 2010.

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