Particle Learning for Fat-Tailed Distributions

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dc.contributor.authorHEDIBERT FREITAS LOPES
dc.contributor.authorPolson, Nicholas G.
dc.coverage.cidadeNão informadopt_BR
dc.coverage.paisNão Informadopt_BR
dc.creatorPolson, Nicholas G.
dc.date.accessioned2022-08-20T12:36:32Z
dc.date.available2022-08-20T12:36:32Z
dc.date.issued2016
dc.description.otherIt is well known that parameter estimates and forecasts are sensitive to assumptions about the tail behavior of the error distribution. In this article, we develop an approach to sequential inference that also simultaneously estimates the tail of the accompanying error distribution. Our simulation-based approach models errors with a tν-distribution and, as new data arrives, we sequentially compute the marginal posterior distribution of the tail thickness. Our method naturally incorporates fat-tailed error distributions and can be extended to other data features such as stochastic volatility. We show that the sequential Bayes factor provides an optimal test of fat-tails versus normality. We provide an empirical and theoretical analysis of the rate of learning of tail thickness under a default Jeffreys prior. We illustrate our sequential methodology on the British pound/U.S. dollar daily exchange rate data and on data from the 2008–2009 credit crisis using daily S&P500 returns. Our method naturally extends to multivariate and dynamic panel data.pt_BR
dc.format.extentp. 1666-1691pt_BR
dc.format.mediumDigitalpt_BR
dc.identifier.doihttps://doi.org/10.1080/07474938.2015.1092809pt_BR
dc.identifier.issn15324168pt_BR
dc.identifier.urihttps://repositorio.insper.edu.br/handle/11224/4066
dc.identifier.volume35pt_BR
dc.language.isoInglêspt_BR
dc.publisherTaylor & Francis Grouppt_BR
dc.relation.isboundProdução vinculada ao Núcleo de Ciências de Dados e Decisão
dc.relation.ispartofEconometric Reviewspt_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 EDITOR.pt_BR
dc.subject.keywordsBayesian inferencept_BR
dc.subject.keywordsCredit crisispt_BR
dc.subject.keywordsDynamic panel datapt_BR
dc.subject.keywordsKullback-Leiblerpt_BR
dc.subject.keywordsMCMCpt_BR
dc.titleParticle Learning for Fat-Tailed Distributionspt_BR
dc.typejournal article
dspace.entity.typePublication
local.identifier.sourceUrihttps://www.tandfonline.com/doi/full/10.1080/07474938.2015.1092809
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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