Pricing Options and Computing Implied Volatilities using Neural Networks

Shuaiqiang Liu, Cornelis Oosterlee and Sander M. Bohte
Additional contact information
Shuaiqiang Liu: Delft Institute of Applied Mathematics (DIAM), Delft University of Technology, Building 28, Mourik Broekmanweg 6, 2628 XE Delft, The Netherlands
Sander M. Bohte: Centrum Wiskunde & Informatica, Science Park 123, 1098 XG Amsterdam, The Netherlands

Risks, 2019, vol. 7, issue 1, 1-22

Abstract: 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 corresponding numerical methods. With ANNs being universal function approximators, this method trains an optimized ANN on a data set generated by a sophisticated financial model, and runs the trained ANN as an agent of the original solver in a fast and efficient way. We test this approach on three different types of solvers, including the analytic solution for the Black-Scholes equation, the COS method for the Heston stochastic volatility model and Brent’s iterative root-finding method for the calculation of implied volatilities. The numerical results show that the ANN solver can reduce the computing time significantly.

Keywords: machine learning; neural networks; computational finance; option pricing; implied volatility; GPU; Black-Scholes; Heston (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 K2 M2 M4 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (34)

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Working Paper: Pricing options and computing implied volatilities using neural networks (2019)
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