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Theory and Inference for a Markov Switching GARCH ModelLuc BauwensUniversité catholique de Louvain Arie PremingerUniversity of Haifa - Department of Economics; Catholic University of Louvain (UCL) - Center for Operations Research and Econometrics (CORE) J. V. K. RomboutsHEC Montreal; Catholic University of Louvain (UCL) - Center for Operations Research and Econometrics (CORE); Centre interuniversitaire sur le risque, les politiques économiques et l'emploi (CIRPÉE); Center for Interuniversity Research and Analysis on Organization (CIRANO) August 2007 CORE Discussion Paper No. 2007/55 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.
Number of Pages in PDF File: 26 Keywords: GARCH, Markov-switching, Bayesian inference JEL Classification: C11, C22, C52 working papers seriesDate posted: September 6, 2007Suggested CitationContact Information
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