A Deep Learning Based Numerical PDE Method for Option Pricing

Xiang Wang (), Jessica Li () and Jichun Li ()
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Xiang Wang: Nanchang University
Jessica Li: Brown University
Jichun Li: University of Nevada

Computational Economics, 2023, vol. 62, issue 1, No 6, 149-164

Abstract: Abstract Proper pricing of options in the financial derivative market is crucial. For many options, it is often impossible to obtain analytical solutions to the Black–Scholes (BS) equation. Hence an accurate and fast numerical method is very beneficial for option pricing. In this paper, we use the Physics-Informed Neural Networks (PINNs) method recently developed by Raissi et al. (J Comput Phys 378:686–707, 2019) to solve the BS equation. Many experiments have been carried out for solving various option pricing models. Compared with traditional numerical methods, the PINNs based method is simple in implementation, but with comparable accuracy and computational speed, which illustrates a promising potential of deep neural networks for solving more complicated BS equations.

Keywords: Option pricing; Black–Scholes equation; Deep learning; Neural networks (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (4)

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DOI: 10.1007/s10614-022-10279-x

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