Efficient Bayesian Inference for Multivariate Factor Stochastic Volatility Models

dc.contributor.authorKastner, Gregor
dc.contributor.authorFrühwirth-Schnatter, Sylvia
dc.contributor.authorHEDIBERT FREITAS LOPES
dc.coverage.cidadeSão Paulopt_BR
dc.coverage.paisBrasilpt_BR
dc.creatorKastner, Gregor
dc.creatorFrühwirth-Schnatter, Sylvia
dc.date.accessioned2023-07-20T16:07:19Z
dc.date.available2023-07-20T16:07:19Z
dc.date.issued2016
dc.description.abstractWe discuss efficient Bayesian estimation of dynamic covariance matrices in multivariate time series through a factor stochastic volatility model. In particular, we propose two interweaving strategies (Yu and Meng, 2011) to substantially accelerate convergence and mixing of standard MCMC approaches. Similar to marginal data augmentation techniques, the proposed acceleration procedures exploit non-identifiability issues which frequently arise in factor models. Our new interweaving strategies are easy to implement and come at almost no extra computational cost; nevertheless, they can boost estimation efficiency by several orders of magnitude as is shown in extensive simulation studies. To conclude, the application of our algorithm to a 26-dimensional exchange rate data set illustrates the superior performance of the new approach for real-world data.
dc.description.otherWe discuss efficient Bayesian estimation of dynamic covariance matrices in multi variate time series through a factor stochastic volatility model. In particular, we pro pose two interweaving strategies (Yu and Meng, 2011) to substantially accelerate con vergence and mixing of standard MCMC approaches. Similar to marginal data aug mentation techniques, the proposed acceleration procedures exploit non-identifiability issues which frequently arise in factor models. Our new interweaving strategies are easy to implement and come at almost no extra computational cost; nevertheless, they can boost estimation efficiency by several orders of magnitude as is shown in extensive simulation studies. To conclude, the application of our algorithm to a 26-dimensional exchange rate data set illustrates the superior performance of the new approach for real-world data.pt_BR
dc.format.extent29 p.pt_BR
dc.format.mediumDigitalpt_BR
dc.identifier.issueBEWP 234/2016
dc.identifier.urihttps://repositorio.insper.edu.br/handle/11224/5894
dc.language.isoInglêspt_BR
dc.publisherInsperpt_BR
dc.relation.ispartofseriesInsper Working Paperpt_BR
dc.rights.licenseO INSPER E ESTE REPOSITÓRIO NÃO DETÊM OS DIREITOS DE USO E REPRODUÇÃO DOS CONTEÚDOS AQUI REGISTRADOS. É RESPONSABILIDADE DO USUÁRIO VERIFICAR OS USOS PERMITIDOS NA FONTE ORIGINAL, RESPEITANDO-SE OS DIREITOS DE AUTOR OU EDITORpt_BR
dc.subject.keywordsAncillarity-sufficiency interweaving strategy (ASIS)pt_BR
dc.subject.keywordsCurse of dimensionalitypt_BR
dc.subject.keywordsData augmentationpt_BR
dc.subject.keywordsDynamic covariance matricespt_BR
dc.subject.keywordsExchange rate datapt_BR
dc.subject.keywordsMarkov chain Monte Carlo (MCMC)pt_BR
dc.titleEfficient Bayesian Inference for Multivariate Factor Stochastic Volatility Modelspt_BR
dc.typeworking paper
dspace.entity.typePublication
local.subject.cnpqCiências Exatas e da Terrapt_BR
local.typeWorking Paperpt_BR
relation.isAuthorOfPublication41f844cb-0e5a-4ef1-bb19-5ab1cec8e2ca
relation.isAuthorOfPublication.latestForDiscovery41f844cb-0e5a-4ef1-bb19-5ab1cec8e2ca

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