OptionMC: A Python Package for Monte Carlo Pricing of European Options
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This article presents OptionMC, a Python package designed for educational purposes that implements Monte Carlo methods for European option pricing. We describe the package's architecture and demonstrate its application through systematic testing against established Black-Scholes analytical solutions. The implementation supports both standard Monte Carlo estimation and variance reduction via antithetic variates, allowing examination of convergence patterns and computational efficiency. Our results suggest that Monte Carlo estimates converge toward analytical solutions as the number of iterations increases, with convergence behavior generally consistent with theoretical expectations. Analysis of parameter sensitivity indicates the package appropriately captures fundamental pricing relationships, including volatility effects, time decay, and moneyness considerations. The distributional characteristics of simulated stock prices and option payoffs align reasonably well with theoretical predictions. While OptionMC primarily serves pedagogical objectives rather than high-performance applications, it offers a transparent framework that may benefit students and researchers seeking to understand the practical implementation of option pricing algorithms through Monte Carlo techniques.
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