Physics Informed Neural Network for Option Pricing
Ashish Dhiman Yibei Hu
We apply a physics-informed deep-learning approach the PINN approach to the
Black-Scholes equation for pricing American and European options. We test our
approach on both simulated as well as real market data, compare it to
analytical/numerical benchmarks. Our model is able to accurately capture the
price behaviour on simulation data, while also exhibiting reasonable
performance for market data. We also experiment with the architecture and
learning process of our PINN model to provide more understanding of convergence
and stability issues that impact performance.