Time-varying extreme pattern with dynamic models

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dc.contributor.authorNascimento, Fernando Ferraz do
dc.contributor.authorGamerman, Dani
dc.contributor.authorHEDIBERT FREITAS LOPES
dc.coverage.cidadeNão informadopt_BR
dc.coverage.paisNão Informadopt_BR
dc.creatorNascimento, Fernando Ferraz do
dc.creatorGamerman, Dani
dc.date.accessioned2022-08-19T22:09:53Z
dc.date.available2022-08-19T22:09:53Z
dc.date.issued2015
dc.description.otherThis paper is concerned with the analysis of time series data with time-varying extreme pattern. This is achieved via a model formulation that considers separately the central part and the tail of the distributions, using a two-component mixture model. Extremes beyond a threshold are assumed to follow a generalized Pareto distribution (GPD). Temporal dependence is induced by allowing the GPD parameters to vary with time. Temporal variation and dependence is introduced at a latent level via the novel use of dynamic linear models (DLM). Novelty lies in the time variation of the shape and scale parameter of the resulting distribution. These changes in limiting regimes as time changes reflect better the data behavior, with important gains in estimation and interpretation. The central part follows a nonparametric mixture approach. The uncertainty about the threshold is explicitly considered. Posterior inference is performed through Markov Chain Monte Carlo (MCMC) methods. A variety of scenarios can be entertained and include the possibility of alternation of presence and absence of a finite upper limit of the distribution for different time periods. Simulations are carried out in order to analyze the performance of our proposed model. We also apply the proposed model to financial time series: returns of Petrobrás stocks and SP500 index. Results show advantage of our proposal over currently entertained models such as stochastic volatility, with improved estimation of high quantiles and extremes.pt_BR
dc.format.extentp. 131-149pt_BR
dc.format.mediumDigitalpt_BR
dc.identifier.doi10.1007/s11749-015-0444-4pt_BR
dc.identifier.urihttps://repositorio.insper.edu.br/handle/11224/4065
dc.identifier.volume25pt_BR
dc.language.isoInglêspt_BR
dc.publisherSpringerpt_BR
dc.relation.isboundProdução vinculada ao Núcleo de Ciências de Dados e Decisão
dc.relation.ispartofSpringerpt_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.keywordsGPDpt_BR
dc.subject.keywordsBayesianpt_BR
dc.subject.keywordsNonparametricpt_BR
dc.subject.keywordsMCMCpt_BR
dc.titleTime-varying extreme pattern with dynamic modelspt_BR
dc.typejournal article
dspace.entity.typePublication
local.identifier.sourceUrihttps://link.springer.com/article/10.1007/s11749-015-0444-4
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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