Particle Filters and Bayesian Inference in Financial Econometrics

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dc.contributor.authorHEDIBERT FREITAS LOPES
dc.contributor.authorTsay, Ruey S.
dc.coverage.cidadeNão informadopt_BR
dc.coverage.paisNão Informadopt_BR
dc.creatorTsay, Ruey S.
dc.date.accessioned2022-10-04T20:13:38Z
dc.date.available2022-10-04T20:13:38Z
dc.date.issued2011
dc.description.otherIn this paper we review sequential Monte Carlo (SMC) methods, or particle fi lters (PF), with special emphasis on its potential applications in fi nancial time series analysis and econometrics. We start with the well-known normal dynamic linear model, also known as the normal linear state space model, for which sequential state learning is available in closed form via standard Kalman fi lter and Kalman smoother recursions. Particle fi lters are then introduced as a set of Monte Carlo schemes that enable Kalman-type recursions when normality or linearity or both are abandoned. The seminal bootstrap fi lter (BF) of Gordon, Salmond and Smith (1993) is used to introduce the SMC jargon, potentials and limitations. We also review the literature on parameter learning, an area that started to attract much attention from the particle fi lter community in recent years. We give particular attention to the Liu–West fi lter (2001), Storvik fi lter (2002) and particle learning (PL) of Carvalho, Johannes, Lopes and Polson (2010). We argue that the BF and the auxiliary particle fi lter (APF) of Pitt and Shephard (1999) defi ne two fundamentally distinct directions within the particle fi lter lit erature. We also show that the distinction is more pronounced with parameter learning and argue that PL, which follows the APF direction, is an attractive extension. One of our contributions is to sort out the research from BF to APF (during the 1990s), from APF to now (the 2000s) and from Liu–West fi lter to Storvik fi lter to PL. To this end, we provide code in R for all the examples of the paper. Readers are invited to fi nd their own way into this dynamic and active research arena.pt_BR
dc.format.extentp. 168-209pt_BR
dc.format.mediumDigitalpt_BR
dc.identifier.doi10.1002/for.1195pt_BR
dc.identifier.urihttps://repositorio.insper.edu.br/handle/11224/4129
dc.identifier.volume30pt_BR
dc.language.isoInglêspt_BR
dc.publisherJohn Wiley & Sons, Ltd.pt_BR
dc.relation.ispartofJournal of Forecastingpt_BR
dc.rights.licenseO INSPER E ESTE REPOSITÓRIO NÃO DETÊM OS DIREITOS DE USO E REPRODUÇÃO DOS CONTEÚDOS AQUI REGISTRADOS. É RESPONSABILIDADE DOS USUÁRIOS INDIVIDUAIS VERIFICAR OS USOS PERMITIDOS NA FONTE ORIGINAL, RESPEITANDO-SE OS DIREITOS DE AUTOR OU EDITORpt_BR
dc.subject.keywordsparticle learningpt_BR
dc.subject.keywordssequential Monte Carlopt_BR
dc.subject.keywordsMarkov chain Monte Carlopt_BR
dc.subject.keywordsstochastic volatilitypt_BR
dc.subject.keywordsrealized volatilitypt_BR
dc.subject.keywordsNelson-Siegel modelpt_BR
dc.titleParticle Filters and Bayesian Inference in Financial Econometricspt_BR
dc.typejournal article
dspace.entity.typePublication
local.subject.cnpqCiências Sociais Aplicadaspt_BR
local.typeArtigo Científicopt_BR
relation.isAuthorOfPublication41f844cb-0e5a-4ef1-bb19-5ab1cec8e2ca
relation.isAuthorOfPublication.latestForDiscovery41f844cb-0e5a-4ef1-bb19-5ab1cec8e2ca

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