# Gated Neural Networks for Option Pricing: Rationality by Design

@inproceedings{Yang2017GatedNN, title={Gated Neural Networks for Option Pricing: Rationality by Design}, author={Yongxin Yang and Yu Zheng and Timothy M. Hospedales}, booktitle={AAAI}, year={2017} }

We propose a neural network approach to price EU call options that significantly outperforms some existing pricing models and comes with guarantees that its predictions are economically reasonable. To achieve this, we introduce a class of gated neural networks that automatically learn to divide-and-conquer the problem space for robust and accurate pricing. We then derive instantiations of these networks that are 'rational by design' in terms of naturally encoding a valid call option surface… Expand

#### Supplemental Code

#### Figures, Tables, and Topics from this paper

#### 23 Citations

Deep Smoothing of the Implied Volatility Surface

- Economics, Computer Science
- NeurIPS
- 2020

A neural network approach to fit and predict implied volatility surfaces (IVSs) by guaranteeing the absence of arbitrage opportunities by penalizing the loss using soft constraints and exploring how deeper NNs improve over shallower ones, as well as other properties of the network architecture. Expand

Formulation Of A Rational Option Pricing Model using Artificial Neural Networks

- 2020

This paper inquires on the options pricing modeling using Artificial Neural Networks to price Apple(AAPL) European Call Options. Our model is based on the premise that Artificial Neural Networks can… Expand

Gated deep neural networks for implied volatility surfaces

- Computer Science
- ArXiv
- 2019

The developed neural network model outperforms the widely used surface stochastic volatility inspired (SSVI) model and other benchmarked neural network models on the mean average percentage error in both in-sample and out-of-sample datasets. Expand

Artificial Neural Networks in Option Pricing

- Computer Science
- 2019

A new technique of adding rational prediction assumptions to neural network prediction is tested and the thesis shows the importance of adding virtual options fulfilling these assumptions in order to achieve better training of the neural network. Expand

Formulation Of A Rational Option Pricing Model using Artificial Neural Networks

- SoutheastCon 2021
- 2021

This paper inquires on the options pricing modeling using Artificial Neural Networks to price Apple's European Call Options. The model is based on the premise that ANNs can be used as functional… Expand

Pricing options and computing implied volatilities using neural networks

- Economics, Computer Science
- Risks
- 2019

A data-driven approach, by means of an Artificial Neural Network, to value financial options and to calculate implied volatilities with the aim of accelerating the corresponding numerical methods. Expand

Delft University of Technology Pricing Options and Computing Implied Volatilities using Neural Networks

- 2019

This paper proposes a data-driven approach, by means of an Artificial Neural Network (ANN), to value financial options and to calculate implied volatilities with the aim of accelerating the… Expand

Neural Networks for Option Pricing and Hedging: A Literature Review

- Economics, Computer Science
- ArXiv
- 2019

This note intends to provide a comprehensive review of neural networks as a nonparametric method for option pricing and hedging since the early 1990s in terms of input features, output variables, benchmark models, performance measures, data partition methods, and underlying assets. Expand

ARTIFICIAL NEURAL NETWORKS PERFORMANCE IN WIG20 INDEX OPTIONS PRICING

- 2020

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 historical… Expand

Pricing options with exponential Lévy neural network

- Computer Science, Economics
- Expert Syst. Appl.
- 2019

The ELNN is the first applicable non-parametric exponential Levy model by virtue of outstanding researches on optimization in the field of ANN, which can improve ANN-based models to avoid several essential issues such as unacceptable outcomes and inconsistent pricing of over-the-counter products. Expand

#### References

SHOWING 1-10 OF 51 REFERENCES

Option Pricing With Modular Neural Networks

- Computer Science, Medicine
- IEEE Transactions on Neural Networks
- 2009

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

Pricing and Hedging Derivative Securities with Neural Networks and a Homogeneity Hint

- Mathematics
- 2000

We estimate a generalized option pricing formula that has a functional shape similar to the usual Black-Scholes formula by a feedforward neural network model. This functional shape is obtained when… Expand

A neural network model for estimating option prices

- Computer Science
- Applied Intelligence
- 2004

A neural network model that processes financial input data is developed to estimate the market price of options at closing. The network's ability to estimate closing prices is compared to the… Expand

Incorporating Second-Order Functional Knowledge for Better Option Pricing

- Computer Science
- NIPS
- 2000

A class of functions similar to multi-layer neural networks but that has those properties of a universal approximator of continuous functions with these and other properties is proposed and applied to the task of modeling the price of call options. Expand

Option pricing when underlying stock returns are discontinuous

- Economics
- 1976

Abstract The validity of the classic Black-Scholes option pricing formula depends on the capability of investors to follow a dynamic portfolio strategy in the stock that replicates the payoff… Expand

A Jump-Diffusion Model for Option Pricing

- Economics, Computer Science
- Manag. Sci.
- 2002

A double exponential jump-diffusion model is proposed, for the purpose of option pricing, which is simple enough to produce analytical solutions for a variety of option-pricing problems, including call and put options, interest rate derivatives, and path-dependent options. Expand

A Closed-Form Solution for Options with Stochastic Volatility with Applications to Bond and Currency Options

- Economics
- 1993

I use a new technique to derive a closed-form solution for the price of a European call option on an asset with stochastic volatility. The model allows arbitrary correlation between volatility and… Expand

Stochastic Finance: An Introduction in Discrete Time

- Mathematics
- 2002

This book is an introduction to financial mathematics. It is intended for graduate students in mathematics and for researchers working in academia and industry. The focus on stochastic models in… Expand

The valuation of warrants: Implementing a new approach

- Economics
- 1977

Abstract The option pricing model developed by Black and Scholes and extended by Merton gives rise to partial differential equations governing the value of an option. When the underlying stock pays… Expand

Option pricing: A simplified approach☆

- Economics
- 1979

This paper presents a simple discrete-time model for valuing options. The fundamental economic principles of option pricing by arbitrage methods are particularly clear in this setting. Its… Expand