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Article: A stochastic volatility model with Markov switching

TitleA stochastic volatility model with Markov switching
Authors
So, MKPLam, KLi, WK
KeywordsARCH model
Bayesian inference
Data augmentation
Gibbs sampling
Monte Carlo Markov chain
Issue Date1998
PublisherAmerican Statistical Association. The Journal's web site is located at http://www.amstat.org/publications/jbes/index.cfm?fuseaction=main
Citation
Journal Of Business And Economic Statistics, 1998, v. 16 n. 2, p. 244-253 How to Cite?
DOI: http://dx.doi.org/10.2307/1392580
AbstractThis article presents a new way of modeling time-varying volatility. We generalize the usual stochastic volatility models to encompass regime-switching properties. The unobserved state variables are governed by a first-order Markov process. Bayesian estimators are constructed by Gibbs sampling. High-, medium- and low-volatility states are identified for the Standard and Poor's 500 weekly return data. Persistence in volatility is explained by the persistence in the low- and the medium-volatility states. The high-volatility regime is able to capture the 1987 crash and overlap considerably with four U.S. economic recession periods.
Persistent Identifierhttp://hdl.handle.net/10722/82744
ISSN
0735-0015
2023 Impact Factor: 2.9
2023 SCImago Journal Rankings: 3.385
ISI Accession Number ID
WOS:000072727300016
References
References in Scopus

 

DC FieldValueLanguage
dc.contributor.authorSo, MKPen_HK
dc.contributor.authorLam, Ken_HK
dc.contributor.authorLi, WKen_HK
dc.date.accessioned2010-09-06T08:32:55Z-
dc.date.available2010-09-06T08:32:55Z-
dc.date.issued1998en_HK
dc.identifier.citationJournal Of Business And Economic Statistics, 1998, v. 16 n. 2, p. 244-253en_HK
dc.identifier.issn0735-0015en_HK
dc.identifier.urihttp://hdl.handle.net/10722/82744-
dc.description.abstractThis article presents a new way of modeling time-varying volatility. We generalize the usual stochastic volatility models to encompass regime-switching properties. The unobserved state variables are governed by a first-order Markov process. Bayesian estimators are constructed by Gibbs sampling. High-, medium- and low-volatility states are identified for the Standard and Poor's 500 weekly return data. Persistence in volatility is explained by the persistence in the low- and the medium-volatility states. The high-volatility regime is able to capture the 1987 crash and overlap considerably with four U.S. economic recession periods.en_HK
dc.languageengen_HK
dc.publisherAmerican Statistical Association. The Journal's web site is located at http://www.amstat.org/publications/jbes/index.cfm?fuseaction=mainen_HK
dc.relation.ispartofJournal of Business and Economic Statisticsen_HK
dc.subjectARCH modelen_HK
dc.subjectBayesian inferenceen_HK
dc.subjectData augmentationen_HK
dc.subjectGibbs samplingen_HK
dc.subjectMonte Carlo Markov chainen_HK
dc.titleA stochastic volatility model with Markov switchingen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0735-0015&volume=16&issue=2&spage=244&epage=253&date=1998&atitle=A+stochastic+volatility+model+with+markov+switchingen_HK
dc.identifier.emailLi, WK: hrntlwk@hku.hken_HK
dc.identifier.authorityLi, WK=rp00741en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.2307/1392580-
dc.identifier.scopuseid_2-s2.0-0032333297en_HK
dc.identifier.hkuros31220en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0032333297&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume16en_HK
dc.identifier.issue2en_HK
dc.identifier.spage244en_HK
dc.identifier.epage253en_HK
dc.identifier.isiWOS:000072727300016-
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridSo, MKP=7004473851en_HK
dc.identifier.scopusauthoridLam, K=36492945700en_HK
dc.identifier.scopusauthoridLi, WK=14015971200en_HK
dc.identifier.issnl0735-0015-

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