Theory and Inference for a Markov Switching GARCH Model
Université catholique de Louvain
University of Haifa - Department of Economics; Catholic University of Louvain (UCL) - Center for Operations Research and Econometrics (CORE)
J. V. K. Rombouts
HEC 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)
CORE Discussion Paper No. 2007/55
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, C52working papers series
Date posted: September 6, 2007
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