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Πεδίο DCΤιμήΓλώσσα
dc.contributor.authorPlatanios, Emmanouil Antonios-
dc.contributor.authorChatzis, Sotirios P.-
dc.contributor.otherΧατζής, Σωτήριος Π.-
dc.date.accessioned2016-01-18T12:07:35Z-
dc.date.available2016-01-18T12:07:35Z-
dc.date.issued2014-05-
dc.identifier.citationIEEE Transactions on Pattern Analysis and Machine Intelligence, 2014, vol. 36, no. 5, pp. 888-900en_US
dc.identifier.issn01628828-
dc.identifier.urihttps://hdl.handle.net/20.500.14279/8204-
dc.description.abstractGeneralized autoregressive conditional heteroscedasticity (GARCH) models have long been considered as one of the most successful families of approaches for volatility modeling in financial return series. In this paper, we propose an alternative approach based on methodologies widely used in the field of statistical machine learning. Specifically, we propose a novel nonparametric Bayesian mixture of Gaussian process regression models, each component of which models the noise variance process that contaminates the observed data as a separate latent Gaussian process driven by the observed data. This way, we essentially obtain a Gaussian process-mixture conditional heteroscedasticity (GPMCH) model for volatility modeling in financial return series. We impose a nonparametric prior with power-law nature over the distribution of the model mixture components, namely the Pitman-Yor process prior, to allow for better capturing modeled data distributions with heavy tails and skewness. Finally, we provide a copula-based approach for obtaining a predictive posterior for the covariances over the asset returns modeled by means of a postulated GPMCH model. We evaluate the efficacy of our approach in a number of benchmark scenarios, and compare its performance to state-of-the-art methodologies.en_US
dc.formatpdfen_US
dc.language.isoenen_US
dc.relation.ispartofIEEE Transactions on Pattern Analysis and Machine Intelligenceen_US
dc.rights© IEEEen_US
dc.subjectGaussian processen_US
dc.subjectPitman-Yor processen_US
dc.subjectMixture modelen_US
dc.subjectConditional heteroscedasticityen_US
dc.subjectCopulaen_US
dc.subjectVolatility modelingen_US
dc.titleGaussian process-mixture conditional heteroscedasticityen_US
dc.typeArticleen_US
dc.collaborationCarnegie Mellon Universityen_US
dc.collaborationCyprus University of Technologyen_US
dc.subject.categoryMathematicsen_US
dc.journalsSubscriptionen_US
dc.reviewPeer Revieweden
dc.countryUnited Statesen_US
dc.countryCyprusen_US
dc.subject.fieldNatural Sciencesen_US
dc.identifier.doi10.1109/TPAMI.2013.183en_US
dc.dept.handle123456789/134en
dc.relation.issue5en_US
dc.relation.volume36en_US
cut.common.academicyear2013-2014en_US
dc.identifier.spage888en_US
dc.identifier.epage900en_US
item.fulltextNo Fulltext-
item.languageiso639-1en-
item.grantfulltextnone-
item.openairecristypehttp://purl.org/coar/resource_type/c_6501-
item.cerifentitytypePublications-
item.openairetypearticle-
crisitem.journal.journalissn1939-3539-
crisitem.journal.publisherIEEE-
crisitem.author.deptDepartment of Electrical Engineering, Computer Engineering and Informatics-
crisitem.author.facultyFaculty of Engineering and Technology-
crisitem.author.orcid0000-0002-4956-4013-
crisitem.author.parentorgFaculty of Engineering and Technology-
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