By implementing Monte Carlo simulations for both the Black-Scholes and Heston option pricing models, we were able to qualitatively compare the models’ sensitivities to volatility as well as strike. Through optimization of our MATLAB code, we were able to compute options prices quickly under both models. Using the closed-form solution for Black-Scholes we analyzed the accuracy of the Monte Carlo method, which we found to be accurate with a large number of sample paths. We then looked into calibrating both models. While calibrating the Heston model is quite difficult, we were successful in calibrating the Black-Scholes model’s volatility parameter using historical data from the previous year. However, it was clear that the volatility calculated this way does not align with the market’s sentiment of the volatility, also known as the implied volatility. By graphing price bands for a year using the implied volatility, we proved that Black-Scholes is not a robust model under extreme market conditions such as a crash. Overall, our research into these financial models provided insight into how the markets price options as well as the tradeoffs between complex models and practicality. No mathematical formula to perfectly predict the future behavior of financial markets exists; but by using financial models and understanding the limitations associated with them, better economic decisions can be made.