A Bayesian MCMC Algorithm for Markov Switching GARCH models
Dhiman Das () and
Byoung Hark Yoo ()
No 179, Econometric Society 2004 North American Summer Meetings from Econometric Society
Markov switching GARCH models have been developed in order to address the statistical regularity observed in financial time series such as strong persistence of conditional variance. However, Maximum Likelihood Estimation faces a implementation problem since the conditional variance depends on all the past history of state. This paper shows that this problem can be handled easily in Bayesian inference. A new Markov Chain Monte Carlo algorithm is introduced and proves to work well in a numerical example.
Keywords: Markov Switching; GARCH; Bayesian (search for similar items in EconPapers)
JEL-codes: C11 (search for similar items in EconPapers)
References: Add references at CitEc
Citations: View citations in EconPapers (3) Track citations by RSS feed
There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.
Working Paper: A Bayesian MCMC Algorithm for Markov Switching GARCH models (2004)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:ecm:nasm04:179
Access Statistics for this paper
More papers in Econometric Society 2004 North American Summer Meetings from Econometric Society Contact information at EDIRC.
Bibliographic data for series maintained by Christopher F. Baum ().