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Theory and inference for a Markov switching GARCH model
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Theory and inference for a Markov switching GARCH model
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Theory and inference for a Markov switching GARCH model
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Journal Article
Theory and inference for a Markov switching GARCH model
2010
Overview
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.
