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dc.contributor.authorCasarin, Roberto
dc.contributor.authorTrecroci, Carmine
dc.date.accessioned2011-07-27T15:57:41Z
dc.date.available2011-07-27T15:57:41Z
dc.date.issued2006
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/6830
dc.language.isoenen
dc.subjectMarkov Switchingen
dc.subjectStochastic Volatilityen
dc.subjectBusiness Cycleen
dc.subjectEquity Marketen
dc.subjectParticle Filtersen
dc.subjectSequential Monte Carloen
dc.subject.ddc519en
dc.subject.classificationjelC11en
dc.subject.classificationjelC15en
dc.subject.classificationjelC22en
dc.subject.classificationjelC63en
dc.subject.classificationjelG10en
dc.subject.classificationjelE32en
dc.subject.classificationjelE44en
dc.titleBusiness Cycle and Stock Market Volatility: A Particle Filter Approachen
dc.typeDocument de travail / Working paper
dc.description.abstractenThe recent observed decline of business cycle variability suggests that broad macroeconomic risk may have fallen as well. This may in turn have some impact on equity risk premia. We investigate the latent structures in the volatilities of the business cycle and stock market valuations by estimating a Markov switching stochastic volatility model. We propose a sequential Monte Carlo technique for the Bayesian inference on both the unknown parameters and the latent variables of the hidden Markov model. Sequential importance sampling is used for filtering the latent variables and kernel estimator with a multiple-bandwidth is employed to reconstruct the parameter posterior distribution. We find that the switch to lower variability has occurred in both business cycle and stock market variables along similar patterns.en
dc.publisher.nameUniversité Paris-Dauphineen
dc.publisher.cityParisen
dc.identifier.citationpages44en
dc.relation.ispartofseriestitleCahiers du CEREMADEen
dc.relation.ispartofseriesnumber2006-10en
dc.description.sponsorshipprivateouien
dc.subject.ddclabelProbabilités et mathématiques appliquéesen


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