Artificial Neural Network for Option Pricing with and Without Asymptotic Correction

Posted: 10 Oct 2019 Last revised: 14 Apr 2020

See all articles by Hideharu Funahashi

Hideharu Funahashi

Mizuho Securities Co. Ltd

Ken Uzawa

Mizuho Securities

Date Written: September 29, 2019

Abstract

This paper proposes a mixed approach of asymptotic expansion (AE) and artificial neural network (ANN) methods for option pricing in order to improve computational speed, stability, and approximation accuracy. In practice, there is wide use of complex stochastic volatility models (SVMs) which can allow for skew and smile shapes. However, under these models, it is usually hard to obtain analytical solutions for options written on the asset price. AE can compute option prices and their sensitivities effectively, but it can usually only compute a finite sum of terms of the complete of the solution because, as the expansion order increases, both analytical and numerical calculations become tedious and messy and the computational cost grows exponentially. On the other hand, using ANN, one can separate the pricing procedure into two steps: (1) approximating ANN that can be trained off-line and (2) using the ANN predicted option price obtained on-line. The off-line procedure has an extremely high computational cost because it requires tens to hundreds of thousands of Monte Carlo (MC) or PDE numerical simulations in order to train several hidden layers and several dozens of nodes. Moreover, deep learning (DL) for option pricing shows unstable and poor quality because the sensitivity of the derivatives price with respect to the input often takes a bell-shape, which induces rapid changes in value. By combining the strong points and making up for the weak points of the two methods, our new approach offers the following improvements: (1) much less training data, layers, and nodes are required: (2) the training becomes more robust: and (3) it speeds up both the off-line and the on-line calculations.

Keywords: artificial neural network,Wiener-Ito chaos expansion, option pricing, stochastic volatility model, mean-reverting process, implied volatility, European option, barrier option

JEL Classification: G12, G13

Suggested Citation

Funahashi, Hideharu and Uzawa, Ken, Artificial Neural Network for Option Pricing with and Without Asymptotic Correction (September 29, 2019). Available at SSRN: https://ssrn.com/abstract=3461104 or http://dx.doi.org/10.2139/ssrn.3461104

Hideharu Funahashi (Contact Author)

Mizuho Securities Co. Ltd ( email )

Tokyo
100-0004
Japan

Ken Uzawa

Mizuho Securities ( email )

Tokyo
100-0004
Japan
M (Phone)

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