Option pricing using Generative Adversarial Networks

This master thesis introduces a novel approach to price financial options, namely a GAN-QMC. It combines a Generative Adversarial Network (GAN) with a Quasi-Monte Carlo (QMC) simulation. The main objective is to improve the accuracy and efficiency of option pricing, specifically focusing on overcoming limitations associated with traditional QMC for option pricing. In this research, GANs are used to model the process of the underlying asset, aiming for a realistic representation of financial data as input to the QMC. The developed GAN-QMC was equally accurate as regular QMC for option pricing and more computationally efficient. Overall, the GAN-QMC is an interesting and accurate option pricing method when distributional assumptions are undesirable.