Full predictivistic modeling of stock market data: Application to change point problems

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dc.contributor.authorLoschi, R. H.
dc.contributor.authorIglesias, P. L.
dc.contributor.authorArellano Valle, R. B.
dc.contributor.authorCruz, F. R. B.
dc.date.accessioned2024-01-10T13:48:38Z
dc.date.available2024-01-10T13:48:38Z
dc.date.issued2007
dc.description.abstractIn change point problems in general we should answer three questions: how many changes are there? Where are they? And, what is the distribution of the data within the blocks? In this paper, we develop a new full predictivistic approach for modeling observations within the same block of observation and consider the product partition model (PPM) for treating the change point problem. The PPM brings more flexibility into the change point problem because it considers the number of changes and the instants when the changes occurred as random variables. A full predictivistic characterization of the model can provide a more tractable way to elicit the prior distribution of the parameters of interest, once prior opinions will be required only about observable quantities. We also present an application to the problem of identifying multiple change points in the mean and variance of a stock market return time series. (c) 2006 Elsevier B.V. All rights reserved.
dc.fechaingreso.objetodigital2024-04-16
dc.format.extent10 páginas
dc.fuente.origenWOS
dc.identifier.doi10.1016/j.ejor.2006.04.016
dc.identifier.issn0377-2217
dc.identifier.urihttps://doi.org/10.1016/j.ejor.2006.04.016
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/79384
dc.identifier.wosidWOS:000244380500018
dc.information.autorucMatemática;Arellano-Valle R;S/I;58107
dc.information.autorucMatemática;Iglesias P;S/I;100265
dc.issue.numero1
dc.language.isoen
dc.nota.accesocontenido parcial
dc.pagina.final291
dc.pagina.inicio282
dc.publisherELSEVIER SCIENCE BV
dc.revistaEUROPEAN JOURNAL OF OPERATIONAL RESEARCH
dc.rightsacceso restringido
dc.subjectuncertainty modeling
dc.subjectnormal-inverse-gamma distribution
dc.subjectproduct partition model
dc.subjectstudent-t distribution
dc.subjectPRODUCT PARTITION MODELS
dc.subject.ods03 Good Health and Well-being
dc.subject.odspa03 Salud y bienestar
dc.titleFull predictivistic modeling of stock market data: Application to change point problems
dc.typeartículo
dc.volumen180
sipa.codpersvinculados58107
sipa.codpersvinculados100265
sipa.indexWOS
sipa.indexScopus
sipa.trazabilidadCarga SIPA;09-01-2024
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