A Statistical Approach to Portfolio Optimization Using Copula-GARCH Models for European Investments

Authors

Jegors Fjodorovs, Institute of Applied Mathematics, Riga Technical UniversityFollow

Author ORCID Identifier

0000-0001-8001-7037

Document Type

Article

Disciplines

Statistics

Publication Details

Statistical and Machine Learning: Methods and Applications (SAML-25) on June 5th and 6th, 2025 at TU Dublin, Ireland.

doi:10.21427/pytf-x051

Abstract

This study explores portfolio optimization using copula functions and GARCH models, focusing on the European stock market. Traditional mean-variance methods often miss dynamic dependencies and tail risks. By applying copula-GARCH models—particularly the Student’s t copula with eGARCH—we better capture volatility asymmetries and tail dependencies. Conditional Value at Risk (CVaR) is used to evaluate downside risk across 10,000 simulated portfolios using high-performance computing. Results show that copula- GARCH models, especially eGARCH, consistently outperform traditional methods in risk-adjusted returns, offering improved risk management.

DOI

https://doi.org/10.21427/pytf-x051

Recommended Citation

Fjodorovs, Jegors, "A Statistical Approach to Portfolio Optimization Using Copula-GARCH Models for European Investments" (2025). SAML-25 Workshop on Statistical and Machine Learning. 14.
https://arrow.tudublin.ie/saml/14

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.