Neural networks for option pricing and hedging: a literature review
This is an incremental review paper summarizing existing research for practitioners and researchers in computational finance.
This paper provides a comprehensive literature review of over a hundred papers on using neural networks as a nonparametric method for option pricing and hedging since the 1990s, comparing them across various dimensions such as input features and performance measures.
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.