risks Article Pricing Options and Computing Implied Volatilities using Neural Networks Shuaiqiang Liu 1,*, Cornelis W. Oosterlee 1,2 and Sander M. Bohte 2 1 Delft Institute of Applied Mathematics (DIAM), Delft University of Technology, Building 28, Mourik Broekmanweg 6, 2628 XE, Delft, Netherlands;
[email protected] 2 Centrum Wiskunde & Informatica, Science Park 123, 1098 XG, Amsterdam, Netherlands;
[email protected] * Correspondence:
[email protected] Received: 8 January 2019; Accepted: 6 February 2019; Published: date 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 1. Introduction In computational finance, numerical methods are commonly used for the valuation of financial derivatives and also in modern risk management. Generally speaking, advanced financial asset models are able to capture nonlinear features that are observed in the financial markets.