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Fecha
2026-03-02
Derechos de acceso
info:eu-repo/semantics/openAccess
Título de la revista
ISSN de la revista
Título del volumen
Editorial
Springer
Citas
Resumen
Dynamic 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.
Descripción
The 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
La 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
La 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
Categorías UNESCO
Palabras clave
Dependence structure, Dynamic correlation, Copula models, Timevarying copula, R code
Citación
Pérez-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
Centro
Facultad de Ciencias Económicas y Empresariales
Departamento
Economía de la Empresa y Contabilidad