A Statistical Approach to Portfolio Optimization Using Copula-GARCH Models for European Investments
Year: 2025
Type: conference-paper
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: 10.21427/pytf-x051
Author: Fjodorovs, Jegors
Institution: Riga Technical University
Language: English
Subfield: Finance
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7080709792
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