Pricing Options and Computing Implied Volatilities using Neural Networks

Pricing Options and Computing Implied Volatilities using Neural Networks

Liu, S. (TU Delft Numerical Analysis)
Oosterlee, C.W. (TU Delft Numerical Analysis; Centrum Wiskunde & Informatica (CWI))
Bohte, Sander M. (Centrum Wiskunde & Informatica (CWI))

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 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.

Black-Scholes
Computational finance
GPU
Heston
Implied volatility
Machine learning
Neural networks
Option pricing

http://resolver.tudelft.nl/uuid:1207904a-5f35-44e6-83d5-2c72071fdc8d

https://doi.org/10.3390/risks7010016

Risks, 7 (1), 1-22

Institutional Repository

journal article

© 2019 S. Liu, C.W. Oosterlee, Sander M. Bohte