Control Variates in Option Pricing

This paper explores the application of Monte Carlo simulation in option pricing, comparing its advantages and limitations to help better understanding the complexities of financial derivatives. Then the paper further investigates the variance reduction strategy known as control variates, which can check how big the error appears in Monte Carlo simulation and improves the accuracy and efficiency of Monte Carlo simulations. This study presents a comprehensive analysis of theoretical foundations of control variates and Monte Carlo simulation, and use Python examples demonstrating the practical applications in financial market. The results highlight the value of sophisticated computational methods for enhancing the accuracy of option pricing and, eventually, facilitating better informed decision-making in a financial environment which is more and more complex now.