Investigating the use of machine learning to value contingent claims

Abstract

A relevant area of finance that has gained traction in recent years is the use of machine learning methods with traditional approaches for pricing European and American options. This dissertation investigates the Cox-Ross-Rubinstein binomial model, the Black-Scholes model, and advanced neural network structures, including artificial neural networks and deep neural networks. By using the Black-Scholes model as a benchmark for European options, and the Cox-Ross-Rubinstein and Least-Squares Monte Carlo model for American options, our research aims to evaluate the accuracy and the efficiency of artificial and deep neural networks in option pricing, in constant and volatile market environments. We show that neural networks perform comparably to traditional models, offering an alternative for financial applications. Model limitations and other areas of improvement are also considered.