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Particle learning for Bayesian non-parametric Markov Switching Stochastic Volatility model

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dc.affiliation.dptoUC3M. Departamento de Estadísticaes
dc.contributor.authorVirbickaite, Audrone
dc.contributor.authorLopes, Hedibert F.
dc.contributor.authorAusín Olivera, María Concepción
dc.contributor.authorGaleano San Miguel, Pedro
dc.contributor.editorUniversidad Carlos III de Madrid. Departamento de Estadísticaes
dc.date.accessioned2014-10-23T15:28:07Z
dc.date.available2014-10-23T15:28:07Z
dc.date.issued2014-10
dc.description.abstractThis paper designs a Particle Learning (PL) algorithm for estimation of Bayesian nonparametric Stochastic Volatility (SV) models for financial data. The performance of this particle method is then compared with the standard Markov Chain Monte Carlo (MCMC) methods for non-parametric SV models. PL performs as well as MCMC, and at the same time allows for on-line type inference. The posterior distributions are updated as new data is observed, which is prohibitively costly using MCMC. Further, a new non-parametric SV model is proposed that incorporates Markov switching jumps.The proposed model is estimated by using PL and tested on simulated data. Finally, the performance of the two non-parametric SV models, with and without Markov switching, is compared by using real financial time series. The results show that including a Markov switching specification provides higher predictive power in the tails of the distribution.en
dc.description.sponsorshipVirbickaite, A. and Ausín, C.M. are grateful for the financial support from MEC grant ECO2011-25706. Galeano, P. acknowledges financial support from MEC grant ECO2012- 38442en
dc.format.mimetypeapplication/pdf
dc.identifier.repecws142819
dc.identifier.urihttps://hdl.handle.net/10016/19576
dc.identifier.uxxiDT/0000001284es
dc.language.isoenges
dc.relation.ispartofseriesUC3M Working papers. Statistics and Econometricsen
dc.relation.ispartofseries14-19
dc.relation.projectIDGobierno de España. ECO2012-38442es
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.accessRightsopen accessen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subject.otherDirichlet Process Mixtureen
dc.subject.otherMarkov Switchingen
dc.subject.otherMCMCen
dc.subject.otherParticle Learningen
dc.subject.otherStochastic Volatilityen
dc.subject.otherSequential Monte Carloen
dc.titleParticle learning for Bayesian non-parametric Markov Switching Stochastic Volatility modelen
dc.typeworking paper*
dc.type.hasVersionSMUR*
dspace.entity.typePublication
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