An Artificial Neural Network Representation of the SABR Stochastic Volatility Model

24 Pages Posted: 14 Dec 2018

Date Written: November 21, 2018

Abstract

In this article, the Universal Approximation Theorem of Artificial Neural Networks (ANNs) is applied to the SABR stochastic volatility model in order to construct highly efficient representations. Initially, the SABR approximation of Hagan et al. [2002] is considered, then a more accurate integration scheme of McGhee [2011] as well as a two factor finite difference scheme. The resulting ANN calculates 10,000 times faster than the finite difference scheme whilst maintaining a high degree of accuracy. As a result, the ANN dispenses with the need for the commonly used SABR Approximation.

Keywords: Stochastic Volatility, SABR Model, SABR Approximation, SABR Integration Scheme, Artificial Neural Network, Universal Approximation Theorem

JEL Classification: C13, C15, C44, C51, C52, C63, D40, G12, G13, G15, G21, G28, K22, M40

Suggested Citation

McGhee, William A, An Artificial Neural Network Representation of the SABR Stochastic Volatility Model (November 21, 2018). Available at SSRN: https://ssrn.com/abstract=3288882 or http://dx.doi.org/10.2139/ssrn.3288882

William A McGhee (Contact Author)

NatWest Markets ( email )

250 Bishopsgate
London, EC2M 4AA
United Kingdom

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