Sequential Bayesian learning for stochastic volatility with variance-gamma jumps in returns

dc.contributor.authorWarty, Samir P.
dc.contributor.authorHEDIBERT FREITAS LOPES
dc.contributor.authorPolson, Nicholas G.
dc.coverage.cidades.l.pt_BR
dc.coverage.paisNão Informadopt_BR
dc.creatorWarty, Samir P.
dc.creatorPolson, Nicholas G.
dc.date.accessioned2022-08-23T19:15:46Z
dc.date.available2022-08-23T19:15:46Z
dc.date.issued2017
dc.description.otherIn this work, we investigate sequential Bayesian estimation for inference of stochastic volatility with variance-gamma (SVVG) jumps in returns. We develop an estimation algorithm that combines the sequential learning auxiliary particle filter with the par ticle learning filter. Simulation evidence and empirical estimation results indicate that this approach is able to filter latent variances, identify latent jumps in returns, and provide sequential learning about the static parameters of SVVG. We demonstrate comparative performance of the sequential algorithm and off-line Markov Chain Monte Carlo in synthetic and real data applications.pt_BR
dc.format.extentp. 460-479pt_BR
dc.format.mediumDigitalpt_BR
dc.identifier.doi10.1002/asmb.2258pt_BR
dc.identifier.issue4pt_BR
dc.identifier.urihttps://repositorio.insper.edu.br/handle/11224/4097
dc.identifier.volume34pt_BR
dc.language.isoInglêspt_BR
dc.relation.ispartofApplied Stochastic Models in Business and Industrypt_BR
dc.relation.urihttps://onlinelibrary.wiley.com/doi/10.1002/asmb.2258pt_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.keywordsAuxiliary particle filteringpt_BR
dc.subject.keywordsBayesian learningpt_BR
dc.subject.keywordsSequential Monte Carlopt_BR
dc.subject.keywordsStochastic volatilitypt_BR
dc.subject.keywordsVariance gammapt_BR
dc.titleSequential Bayesian learning for stochastic volatility with variance-gamma jumps in returnspt_BR
dc.typeworking paper
dspace.entity.typePublication
local.subject.capesCiências Sociais Aplicadaspt_BR
local.subject.cnpqCiências Sociais Aplicadaspt_BR
local.typeDiscussion Paperpt_BR
relation.isAuthorOfPublication41f844cb-0e5a-4ef1-bb19-5ab1cec8e2ca
relation.isAuthorOfPublication.latestForDiscovery41f844cb-0e5a-4ef1-bb19-5ab1cec8e2ca
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