Theory and Inference for a Markov Switching GARCH Model
Copyright policy )Theory and Inference for a Markov Switching GARCH Model
Summary: 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.
- Université Catholique de Louvain Belgium
- University of Haifa Israel
- Center for Interuniversity Research and Analysis on Organizations Canada
- Prime Minister's Office
- Prime Minister's Office Brunei Darussalam
Applications of statistics to actuarial sciences and financial mathematics, Time series, auto-correlation, regression, etc. in statistics (GARCH), Markov processes: estimation; hidden Markov models, Bayesian inference, Numerical analysis or methods applied to Markov chains, GARCH, Markov-switching, Bayesian inference., GARCH, Markov-switching, Bayesian inference, Statistical methods; risk measures, jel: jel:C52, jel: jel:C11, jel: jel:C22
Applications of statistics to actuarial sciences and financial mathematics, Time series, auto-correlation, regression, etc. in statistics (GARCH), Markov processes: estimation; hidden Markov models, Bayesian inference, Numerical analysis or methods applied to Markov chains, GARCH, Markov-switching, Bayesian inference., GARCH, Markov-switching, Bayesian inference, Statistical methods; risk measures, jel: jel:C52, jel: jel:C11, jel: jel:C22
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