Publicación:
Estimation of Distribution Dependence Structures Using time-varying Copulas in R

Vista sencilla de ítem
dc.contributor.authorPérez-Cambriles, Antonio
dc.contributor.authorBenito Muela, Sonia
dc.contributor.authorLópez Martín, Carmen
dc.contributor.funderUNIVERSIDAD NACIONAL DE EDUCACIÓN A DISTANCIA
dc.date.accessioned2026-03-02T18:14:30Z
dc.date.available2026-03-02T18:14:30Z
dc.date.issued2026-03-02
dc.descriptionThe registered version of this article, first published in Computational Economics, is available online at the publisher's website: Springer, https://doi.org/10.1007/s10614-025-11254-y
dc.descriptionLa versión registrada de este artículo, publicado por primera vez en Computational Economics, está disponible en línea en el sitio web del editor: Springer, https://doi.org/10.1007/s10614-025-11254-y
dc.description.abstractDynamic copulas provide a flexible framework for modelling time-varying dependencies between financial assets, overcoming the limitations of traditional correlation measures and DCC models. Their ability to capture non-linear relationships, tail dependence, and asymmetry makes them particularly useful for risk management and portfolio optimization. The main contributions of this paper are: first, by extending dynamic specifications to the Student’s t, Clayton, and Frank copulas, and second, by providing their implementation in the R environment through the “dynCopula” package, freely available for the research community. Our empirical application considers three major international stock markets (Euro Stoxx 50, S&P 500, Nikkei) and Bitcoin. The results reveal that the dependence between assets evolves over time and intensifies during periods of financial stress. We also show that diversification benefits increase as the degree of dependence decreases, provided that assets have similar risk levels. Finally, dynamic copulas yield more accurate estimates of market risk than static models. These results underscore the benefits of dynamic copulas for practitioners: they enable more reliable risk quantification, enhance hedging strategies, and support the construction of optimal or minimum-variance portfolios under changing market conditions, making them a valuable tool for both investment management and financial risk analysis.en
dc.description.provenanceMade available in DSpace on 2026-03-02T18:14:30Z (GMT). No. of bitstreams: 1 Estimation of Distribution Dependence Structures Using time-varying Copulas in R.pdf: 5209332 bytes, checksum: 61411756fa6f332434a3ad811abbb9e3 (MD5) Previous issue date: 2026-03-02en
dc.description.sponsorshipOpen Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This work was supported by the Young Talent Research Project UNED 2022 (076–044355 ENER-UK) grant.
dc.description.versionversión publicada
dc.identifier.citationPérez-Cambriles, A., Benito-Muela, S. & López-Martín, C. Estimation of Distribution Dependence Structures Using time-varying Copulas in R. Computational Economics (2026). https://doi.org/10.1007/s10614-025-11254-y
dc.identifier.doihttps://doi.org/10.1007/s10614-025-11254-y
dc.identifier.eissn1572-9974
dc.identifier.issn0927-7099
dc.identifier.urihttps://hdl.handle.net/20.500.14468/31994
dc.journal.titleComputational Economics
dc.language.isoen
dc.publisherSpringer
dc.relation.centerFacultad de Ciencias Económicas y Empresariales
dc.relation.departmentEconomía de la Empresa y Contabilidad
dc.relation.projectidinfo:eu-repo/grantAgreement/UNED/Proyecto Talento Joven UNED 2022/076–044355 ENER-UK
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.es
dc.subject53 Ciencias Económicas
dc.subject.keywordsDependence structureen
dc.subject.keywordsDynamic correlationen
dc.subject.keywordsCopula modelsen
dc.subject.keywordsTimevarying copulaen
dc.subject.keywordsR codeen
dc.titleEstimation of Distribution Dependence Structures Using time-varying Copulas in Ren
dc.typeartículoes
dc.typejournal articleen
dspace.entity.typePublication
relation.isAuthorOfPublication01b4ab6d-1510-434b-b108-29b4cc8746f0
relation.isAuthorOfPublicationc97e2d29-4ce5-49e3-b864-9810ead93be6
relation.isAuthorOfPublication.latestForDiscovery01b4ab6d-1510-434b-b108-29b4cc8746f0
Archivos
Bloque original
Mostrando 1 - 1 de 1
Miniatura
Nombre:
Estimation of Distribution Dependence Structures Using time-varying Copulas in R.pdf
Tamaño:
4.97 MB
Formato:
Adobe Portable Document Format
Descargar
Bloque de licencias
Mostrando 1 - 1 de 1
No hay miniatura disponible
Nombre:
license.txt
Tamaño:
3.62 KB
Formato:
Item-specific license agreed to upon submission
Descripción:
Descargar
Colecciones
Artículos y papers