Neural networks for option pricing and hedging: a literature review

TL;DR

This paper provides a comprehensive review of neural network applications in option pricing and hedging, comparing various models, features, and techniques used over the past decades.

Contribution

It systematically analyzes and summarizes the evolution, methodologies, and performance of neural network-based approaches in option pricing and hedging.

Findings

Neural networks have been used since the 1990s for option pricing.

Comparison of models based on features, outputs, and performance measures.

Discussion of regularisation techniques and related work.

Abstract

Neural networks have been used as a nonparametric method for option pricing and hedging since the early 1990s. Far over a hundred papers have been published on this topic. This note intends to provide a comprehensive review. Papers are compared in terms of input features, output variables, benchmark models, performance measures, data partition methods, and underlying assets. Furthermore, related work and regularisation techniques are discussed.