Dependent and Independent Time Series Errors Under Elliptically Countered Models

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dc.contributor.affiliationPérez-Ramirez F.O., Faculty of Engineering, University of Medellin, Medellin, 050026, Colombia, Modeling and Scientific Computing, Faculty of Basic Sciences, University of Medellin, Medellin, 050026, Colombia
dc.contributor.affiliationCaro-Lopera F.J., Faculty of Basic Sciences, University of Medellin, Medellin, 050026, Colombia
dc.contributor.affiliationDíaz-García J.A., Facultad de Zootecnia y Ecología, Universidad Autónoma de Chihuahua, Chihuahua, 31453, Mexico
dc.contributor.affiliationFarías G.G., Instituto de Ciencia de los Datos e Inteligencia Artificial (DATAI), Pamplona, 31009, Spain, Research Center in Mathematics, Probability and Statistics, University of Navarra, Guanajuato, 36023, Mexico
dc.contributor.authorPérez-Ramirez F.O.
dc.contributor.authorCaro-Lopera F.J.
dc.contributor.authorDíaz-García J.A.
dc.contributor.authorFarías G.G.
dc.date.accessioned2025-09-08T14:23:41Z
dc.date.available2025-09-08T14:23:41Z
dc.date.issued2025
dc.descriptionWe explore the impact of time series behavior on model errors when working under an elliptically contoured distribution. By adopting a time series approach aligned with the realistic dependence between errors under such distributions, this perspective shifts the focus from increasingly complex and challenging correlation analyses to volatility modeling that utilizes a novel likelihood framework based on dependent probabilistic samples. With the introduction of a modified Bayesian Information Criterion, which incorporates a ranking of degrees of evidence of significant differences between the compared models, the critical issue of model selection is reinforced, clarifying the relationships among the most common information criteria and revealing limited relevance among the models based on independent probabilistic samples, when tested on a well-established database. Our approach challenges the traditional hierarchical models commonly used in time series analysis, which assume independent errors. The application of rigorous differentiation criteria under this novel perspective on likelihood, based on dependent probabilistic samples, provides a new viewpoint on likelihood that arises naturally in the context of finance, adding a novel result. We provide new results for criterion selection, evidence invariance, and transitions between volatility models and heuristic methods to calibrate nested or non-nested models via convergence properties in a distribution. © 2025 by the authors.
dc.identifier.doi10.3390/econometrics13020022
dc.identifier.instnameinstname:Universidad de Medellínspa
dc.identifier.issn22251146
dc.identifier.reponamereponame:Repositorio Institucional Universidad de Medellínspa
dc.identifier.repourlrepourl:https://repository.udem.edu.co/
dc.identifier.urihttp://hdl.handle.net/11407/9084
dc.language.isoeng
dc.publisher.facultyFacultad de Ingenieríasspa
dc.publisher.facultyFacultad de Ciencias Básicasspa
dc.publisher.programIngeniería de Sistemasspa
dc.relation.citationissue2
dc.relation.citationvolume13
dc.relation.isversionofhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-105009311512&doi=10.3390%2feconometrics13020022&partnerID=40&md5=878b45bec5d03198d013ce64894c5ab6
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dc.rights.accesoRestricted access
dc.rights.accessrightsinfo:eu-repo/semantics/restrictedAccess
dc.sourceEconometrics
dc.sourceEconometrics
dc.sourceScopus
dc.subjectARCH
dc.subjectEGARCH models
dc.subjectElliptically contoured models
dc.subjectGARCH
dc.subjectGrades of evidence for significant difference
dc.subjectSelection criteria
dc.subjectTGARCH
dc.subjectTime series
dc.subjectVolatility models
dc.titleDependent and Independent Time Series Errors Under Elliptically Countered Models
dc.typeArticle
dc.type.localArtículospa
dc.type.versioninfo:eu-repo/semantics/publishedVersion

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