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Neural networks for option pricing and hedging: a literature review
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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.
Forward citations
Cited by 5 Pith papers
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On MUON optimization: From non-convergence to an error analysis with Polar Express and the Newton-Schulz polynomial from implementations
For a one-dimensional quadratic stochastic optimization problem, MUON with Newton-Schulz steps provably fails to converge to the minimizer for all sufficiently large mini-batch sizes when the data is skewed, while a n...
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Statistical Arbitrage in Options Markets by Graph Learning and Synthetic Long Positions
A tree-based graph neural network predicts put-call parity deviations in KOSPI 200 options, and a constrained synthetic long-short arbitrage projection turns those predictions into positive-P&L positions with zero ter...
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Deep Learning Option Pricing with Market Implied Volatility Surfaces
A VAE-compressed volatility surface plus a small neural network can approximate QuantLib prices for American puts and arithmetic Asian options in a single forward pass.
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Deep Learning vs. Black-Scholes: Option Pricing Performance on Brazilian Petrobras Stocks
A residual network trained on eight years of Petrobras option data reports lower mean absolute error than Black-Scholes in the 3-19 BRL range, but the reported 64.3% reduction is contradicted by the paper's own table,...
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Option Pricing Using Ensemble Learning
On CSI 300 index options, gradient boosting ensembles (LGBM, XGBoost, NGBoost) achieve the lowest RMSE in most experiments, but the training set includes data from after the test period, invalidating the temporal real...
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