Option Pricing Based on Black-Scholes Model, Monte Carlo Method and Binomial Tree Model

Abstract

Contemporarily, option is one of the widely implemented underlying assets to hedge the risk of portfolio and satisfy certain trading motivation for investors. To obtain extra return based on investment, it is crucial to utilize and apply an accuracy and suitable tool to price option. On this basis, this study focused on comparing and applying different model to achieve the goal of option pricing. To be specific, the Python code was used to help make calculations and list the results, with three functions to price options with three diverse methods. This paper chose Alphabet Inc., Amazon, Meta Platforms, Spotify, Sunrun, and Tesla, Inc. and priced the options using the historical market data from July 20, 2022, to October 20, 2022. Reasonable results for each option are achieved with differences between each method, offering price ranges for the options. These results offer a guideline for applying different methods to option pricing and compare the effectiveness of the three methods.