A note on Bayesian identification of change points in data sequences

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dc.contributor.authorLoschi, R. H.
dc.contributor.authorCruz, F. R. B.
dc.contributor.authorTakahashi, R. H. C.
dc.contributor.authorIglesias, P. L.
dc.contributor.authorArellano Valle, R. B.
dc.contributor.authorSmith, J. MacGregor
dc.date.accessioned2024-01-10T12:04:14Z
dc.date.available2024-01-10T12:04:14Z
dc.date.issued2008
dc.description.abstractRecent research in mathematical methods for finance suggests that time series for financial data should be studied with nonstationary models and with structural changes that include both jumps and heteroskedasticity (with jumps in variance). It has been recognized that discriminating between variations caused by the continuous motion of Brownian shocks and the genuine discontinuities in the path of the process constitutes a challenge for existing computational procedures. This issue is addressed here, using the product partition model (PPM), for performing such discrimination and the estimation of process jump parameters. Computational implementation aspects of PPM applied to the identification of change points in data sequences are discussed. In particular, we analyze the use of a Gibbs sampling scheme to compute the estimates and show that there is no significant impact of such use on the quality of the results. The influence of the size of the data sequence on the estimates is also of interest, as well as the efficiency of the PPM to correctly identify atypical observations occurring in close instants of time. Extensive Monte Carlo simulations attest to the effectiveness of the Gibbs sampling implementation. An illustrative financial time series example is also presented. (C) 2006 Elsevier Ltd. All rights reserved.
dc.fechaingreso.objetodigital2024-04-02
dc.format.extent15 páginas
dc.fuente.origenWOS
dc.identifier.doi10.1016/j.cor.2006.02.018
dc.identifier.eissn1873-765X
dc.identifier.issn0305-0548
dc.identifier.urihttps://doi.org/10.1016/j.cor.2006.02.018
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/75736
dc.identifier.wosidWOS:000250165500011
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.final170
dc.pagina.inicio156
dc.publisherPERGAMON-ELSEVIER SCIENCE LTD
dc.revistaCOMPUTERS & OPERATIONS RESEARCH
dc.rightsacceso restringido
dc.subjectatypical observations
dc.subjectheteroskedasticity
dc.subjectstructural changes
dc.subjectPRODUCT PARTITION MODEL
dc.subjectEQUILIBRIUM
dc.subjectVOLATILITY
dc.subjectPRICES
dc.subjectJUMPS
dc.subject.ods03 Good Health and Well-being
dc.subject.odspa03 Salud y bienestar
dc.titleA note on Bayesian identification of change points in data sequences
dc.typeartículo
dc.volumen35
sipa.codpersvinculados58107
sipa.codpersvinculados100265
sipa.indexWOS
sipa.indexScopus
sipa.trazabilidadCarga SIPA;09-01-2024
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