Email this article Nonparametric Approach to Regression Modeling Based on Copula Functions: Application to Bivariate Models
- Authors: Bachaev U.A.1
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Affiliations:
- Financial University under the Government of the Russian Federation
- Issue: Vol 21, No 3 (2025)
- Pages: 136-145
- Section: Mathematical, Statistical and Instrumental Methods in Economics
- URL: https://journals.rcsi.science/2541-8025/article/view/313197
- DOI: https://doi.org/10.33693/2541-8025-2025-21-3-136-145
- EDN: https://elibrary.ru/WXJQND
- ID: 313197
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Abstract
This paper explores a nonparametric approach to regression modeling based on copula functions, which overcomes the limitations of classical linear models. The study analyzes the advantages of the copula approach, including the ability to model complex nonlinear, asymmetric, and tail dependencies, as well as the separation of dependence structure and marginal distributions. The main classes of copulas (elliptical and Archimedean), their properties, and their application in bivariate models are discussed. Special attention is paid to practical examples of copula applications in economics and finance. The advantages of the approach, such as flexibility and universality, are highlighted, along with its limitations related to computational complexity and data quality requirements.
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##article.viewOnOriginalSite##About the authors
Umar A. Bachaev
Financial University under the Government of the Russian Federation
Author for correspondence.
Email: UABachaev@fa.ru
ORCID iD: 0000-0003-4109-8596
SPIN-code: 8029-6668
Scopus Author ID: 996707
ResearcherId: AEB-0730-2022
Assistant of the Department of Information Technology; Financial University under the Government of the Russian Federation
Russian Federation, MoscowReferences
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