Temporal dependence in extremes with dynamic model

dc.contributor.authorNascimento, Fernando Ferraz do
dc.contributor.authorGamerman, Dani
dc.contributor.authorHEDIBERT FREITAS LOPES
dc.coverage.cidadeSão Paulopt_BR
dc.coverage.paisBrasilpt_BR
dc.creatorNascimento, Fernando Ferraz do
dc.creatorGamerman, Dani
dc.date.accessioned2023-07-22T14:03:49Z
dc.date.available2023-07-22T14:03:49Z
dc.date.issued2014
dc.description.otherThis paper is concerned with the analysis of time series data with temporal dependence through extreme events. 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 thresh old are assumed to follow a generalized Pareto distribution (GPD). Temporal dependence is induced by allowing to GPD parameter to vary with time. Tem poral 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 nonpara metric 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 Petrobr´as 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.extent21 p.pt_BR
dc.format.mediumDigitalpt_BR
dc.identifier.issueWPE 346/2014
dc.identifier.urihttps://repositorio.insper.edu.br/handle/11224/5911
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.keywordsGPDpt_BR
dc.subject.keywordsBayesianpt_BR
dc.subject.keywordsnonparametricpt_BR
dc.subject.keywordsMCMCpt_BR
dc.titleTemporal dependence in extremes with dynamic modelpt_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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