Advanced Portfolio Optimization Using Copula and GARCH Models: A High-Performance Computing Approach for the European Stock Market

2024 IEEE 65th International Scientific Conference on Information Technology and Management Science of Riga Technical University (ITMS 2024) : Proceedings 2024
Jegors Fjodorovs, Andrejs Matvejevs, Anastasija Vasiļjeva

This paper explores advanced portfolio optimization strategies using copula and GARCH models to enhance risk management and profitability in the European stock market. By utilizing high-performance computing (HPC) to conduct extensive simulations on approximately 10,000 portfolios, the study compares the effectiveness of various copula-GARCH models against traditional approaches, such as the mean-variance model. The most effective configuration—identified as a Student's copula with marginal Student's distribution and an eGARCH model—was employed to simulate returns and construct optimal portfolios that minimize Conditional Value at Risk (CVaR). The scalability and robustness of this approach offer valuable insights into its practical applications for portfolio management.

Keywords
Copula, GARCH, CVaR, portfolio optimization, European stock market, high-performance computing, simulations
DOI
10.1109/ITMS64072.2024.10741934
Hyperlink
https://ieeexplore.ieee.org/document/10741934

Fjodorovs, J., Matvejevs, A., Vasiljeva, A. Advanced Portfolio Optimization Using Copula and GARCH Models: A High-Performance Computing Approach for the European Stock Market. In: 2024 IEEE 65th International Scientific Conference on Information Technology and Management Science of Riga Technical University (ITMS 2024) : Proceedings, Latvia, Riga, 3-4 October, 2024. Piscataway, NJ: IEEE, 2024, pp. 36-40. ISBN 979-8-3315-3384-7. e-ISBN 979-8-3315-3383-0. ISSN 2771-6953. e-ISSN 2771-6937. Available from: doi:10.1109/ITMS64072.2024.10741934

Publication language
English (en)