Artificial Intelligence in Financial Derivatives: A Comparative Analysis of PSO and ACO for American-Style Put Options

Artificial Intelligence (AI) enhances option pricing accuracy and risk management, enabling precise valuation of complex derivatives across various market scenarios. This paper compares Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) in valuing American-style put options. We apply these metaheuristic algorithms to tackle the complexity of option pricing due to the early exercise feature, evaluating their performance across various boundary functions. We assess each method's accuracy and computational efficiency through extensive simulations. These simulations are benchmarked against findings from existing literature. Python's Computer Algebra System (CAS) is used for the simulations. The findings highlight AI's potential to enhance option valuation and contribute valuable insights for practitioners in finance.