Optimizing Conditional Value-at-Risk in Financial Portfolios using Parallel Computing Strategies

This project introduces an innovative perspective to portfolio optimization and financial risk management, thoroughly analyzing the ever-changing and unpredictable structure of financial markets. In recent years in financial literature, risk measures such as Value at Risk (VaR) and Conditional Value at Risk (CVaR) have been acknowledged as suitable metrics in financial risk management. Focusing on CVaR minimization, this project offers users the opportunity to create optimized portfolios aligned with their risk tolerance, providing investors the chance to limit potential losses and maximize returns. Unlike standard portfolio management methodologies, this approach considers portfolio risk from a broader perspective, aiding investors in better understanding the financial risks they face and developing more informed strategies.

Keywords: Conditional Value at Risk; Portfolio Optimization; Monte Carlo Simulation; Parallel Computing