Neural Networks for Option Pricing and Hedging: A Literature Review

@article{Ruf2019NeuralNF,
  title={Neural Networks for Option Pricing and Hedging: A Literature Review},
  author={Johannes Ruf and Weiguan Wang},
  journal={PSN: Other Political Methods: Quantitative Methods (Topic)},
  year={2019}
}
  • J. Ruf, Weiguan Wang
  • Published 2019
  • Economics, Computer Science, Mathematics
  • PSN: Other Political Methods: Quantitative Methods (Topic)
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. 
Hedging with Linear Regressions and Neural Networks
TLDR
A network is designed, named HedgeNet, that directly outputs a hedging strategy that is trained to minimise the hedging error instead of the pricing error and is able to reduce the mean squared hedges error of the Black-Scholes benchmark significantly. Expand
ARTIFICIAL NEURAL NETWORKS PERFORMANCE IN WIG20 INDEX OPTIONS PRICING
In this paper the performance of artificial neural networks in option pricing is analyzed and compared with the results obtained from the Black – Scholes – Merton model based on the historicalExpand
FX Volatility Calibration Using Artificial Neural Networks
Recent years have witnessed a surge of interest in the application of artificial neural networks (ANNs) to the calibration of financial models. In this dissertation we explore two distinct butExpand
Neural network approximation for superhedging prices
This article examines neural network-based approximations for the superhedging price process of a contingent claim in a discrete time market model. First we prove that the α-quantile hedging priceExpand
Deep Calibration of Financial Models: Turning Theory Into Practice
TLDR
This paper provides the first comprehensive empirical study on the application of artificial neural networks for calibration based on observed market data and shows that the results of an ANN based calibration framework are very competitive and derive guidelines for its practical implementation to enhance and accelerate managerial decisions. Expand
On Calibration Neural Networks for extracting implied information from American options
TLDR
It is shown that machine learning can be used as an efficient numerical technique to extract implied information from American options and the inverse function is approximated by an artificial neural network on the computational domain of interest. Expand
Robust Deep Hedging
We study pricing and hedging under parameter uncertainty for a class of Markov processes which we call generalized affine processes and which includes the Black-Scholes model as well as the constantExpand
Deep Hedging under Rough Volatility
TLDR
This work investigates the performance of the Deep Hedging framework under training paths beyond the (finite dimensional) Markovian setup and suggests parsimonious but suitable network architectures capable of capturing the non-Markoviantity of time-series. Expand
The Deep Parametric PDE Method: Application to Option Pricing
TLDR
A single neural network approximates the solution of a whole family of PDEs after being trained without the need of sample solutions and a comparison with alternative machine learning approaches confirms the effectiveness of the approach. Expand
Robust Pricing and Hedging via Neural SDEs
TLDR
Combining neural networks with risk models based on classical stochastic differential equations (SDEs), the resulting model called neural SDE is an instantiation of generative models and is closely linked with the theory of causal optimal transport. Expand
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References

SHOWING 1-10 OF 223 REFERENCES
A neural network approach to option pricing
TLDR
The artificial neural network is trained on the implied volatility rather then the option price, which leads to an improved performance when compared to the competing models. Expand
Valuing and Hedging American Put Options Using Neural Networks
We use a neural network to non-parametrically estimate the market valuation function for American put options with real data. The neural network valuation function is twice di erentiable, andExpand
Efficient Option Pricing via a Globally Regularized Neural Network
TLDR
This paper proposes a novel neural network learning algorithm for option-pricing, which is a nonparametric approach to improve generalization and computing time and demonstrates a significant performance improvement to reduce test error. Expand
Improving the pricing of options: a neural network approach
In this paper we apply statistical inference techniques to build neural network models which are able to explain the prices of call options written on the German stock index DAX. By testing for theExpand
Option Pricing With Modular Neural Networks
TLDR
This paper investigates a nonparametric modular neural network model to price the S&P-500 European call options and concludes that modularity improves the generalization properties of standard feedforward neural network option pricing models. Expand
Option Pricing Using Bayesian Neural Networks
TLDR
Options have provided a field of much study because of the complexity involved in pricing them and the use of neural networks to predict outcomes based on past data has been proposed. Expand
Barrier option pricing: modelling with neural nets
TLDR
Call option pricing for up-and-out style barrier options is reported through the use of a neural net model using the Rubenstein and Reiner analytic model for standard option price data. Expand
Robust Artificial Neural Networks for Pricing of European Options
The option pricing ability of Robust Artificial Neural Networks optimized with the Huber function is compared against those optimized with Least Squares. Comparison is in respect to pricing EuropeanExpand
Option price forecasting using neural networks
In this research, forecasting of the option prices of Nikkei 225 index futures is carried out using backpropagation neural networks. Different results in terms of accuracy are achieved by groupingExpand
Improving Neural Network Based Option Price Forecasting
TLDR
This paper modifies the Black & Scholes model given the price of an option based on the no-arbitrage value of a forward contract, written on the same underlying asset, and derives a modified formula that can be used for this purpose. Expand
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