Neural networks for option pricing and hedging: a literature review

2019/11/13 by Ruf, Johannes, Wang, Weiguan · 7 citations
#91G20 #91G60 #91G70 #91G80 #Computational Finance (q-fin.CP) #FOS: Computer and information sciences #FOS: Economics and business #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Risk Management (q-fin.RM) #Statistical Finance (q-fin.ST)

paper · doi:10.48550/arxiv.1911.05620

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.

Cited by

• Deep Learning-Enhanced Calibration of the Heston Model: A Unified Framework
• Application of Deep Reinforcement Learning to At-the-Money S&P 500 Options Hedging
• Deep learning CAT bond valuation
• Deep Learning Option Pricing with Market Implied Volatility Surfaces
• Statistical Arbitrage in Options Markets by Graph Learning and Synthetic Long Positions
• Distributional Adversarial Attacks and Training in Deep Hedging
• Hedging with memory: shallow and deep learning with signatures

Related

• Option Pricing Using Bayesian Neural Networks
• Finance-Informed Neural Network: Learning the Geometry of Option Pricing
• Can Machine Learning Algorithms Outperform Traditional Models for Option Pricing?
• Deep learning calibration of option pricing models: some pitfalls and solutions
• Hedging option books using neural-SDE market models