EFFECTIVE RISK MANAGEMENT STRATEGIES FOR HEDGING WITH OPTIONS

This thesis investigates the application of options as strategic instruments for effective risk management in volatile financial markets. Amidst increasing uncertainty, it explores the valuation and hedging capabilities of various pricing models: Monte Carlo Simulation and the Black-Scholes Formula for European options, and Least Squares Monte Carlo (LSMC) and the Binomial Tree Method for American options, assuming zero dividend yield for ENI S.p.A. stock. Additionally, the study extends to real option valuation, applying LSMC to Brent Crude data to assess the value of managerial flexibility in capital-intensive projects. Empirical results show strong convergence between Monte Carlo and Black-Scholes prices for European options. For American options, both LSMC and Binomial Tree methods effectively capture the early exercise feature, particularly in put options. Delta estimation reveals deterministic values under Black-Scholes and stochastic variability in simulation-based methods, reflecting the dynamic nature of real-world hedging. The conceptual valuation of a real option based on Brent Crude yields a positive value, illustrating the strategic benefit of flexibility in investment decisions. This thesis contributes to both theory and practice by demonstrating how sophisticated quantitative models can enhance pricing accuracy, risk mitigation, and strategic planning. It offers actionable insights for practitioners and policymakers, highlighting the critical role of flexible, data-driven approaches in navigating complex and volatile markets.