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.
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Paper provided by HEC Montréal, Institut d'économie appliquée in its series Cahiers de recherche with number
07-09.
Length: 30 pages Date of creation: Aug 2007 Date of revision: Handle: RePEc:iea:carech:0709
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