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Bayesian Inference for Generalised Markov Switching Stochastic Volatility Models

dc.contributor.authorCasarin, Roberto
dc.date.accessioned2011-06-23T16:32:04Z
dc.date.available2011-06-23T16:32:04Z
dc.date.issued2003
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/6596
dc.language.isoenen
dc.subjectParticle Filteren
dc.subjectMarkov Switchingen
dc.subjectStochastic Volatilityen
dc.subjectHeavy Tailsen
dc.subject.ddc519en
dc.titleBayesian Inference for Generalised Markov Switching Stochastic Volatility Modelsen
dc.typeCommunication / Conférence
dc.description.abstractenWe study a Markov switching stochastic volatility model with heavy tail innovations in the observable process. Due to the economic interpretation of the hidden volatility regimes, these models have many nancial applications like asset allocation, option pricing and risk management. The Markov switching process is able to capture clustering e ects and jumps in volatility. Heavy tail innovations account for extreme variations in the observed process. Accurate modelling of the tails is important when estimating quantiles is the major interest like in risk management applications. Moreover we follow a Bayesian approach to ltering and estimation, focusing on recently developed simulation based ltering techniques, called Particle Filters. Simulation based lters are recursive techniques, which are useful when assuming non-linear and non-Gaussian latent variable models and when processing data sequentially. They allow to update parameter estimates and state ltering as new observations become available.en
dc.identifier.citationpages47en
dc.description.sponsorshipprivateouien
dc.subject.ddclabelProbabilités et mathématiques appliquéesen
dc.relation.conftitle4th International Workshop on Objective Bayesian Methodologyen
dc.relation.confdate2003-06
dc.relation.confcityAussoisen
dc.relation.confcountryFranceen


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