Physics Informed Neural Network for Option Pricing
This work addresses option pricing in finance, but it is incremental as it applies an existing PINN method to a known equation without major innovations.
The authors tackled the problem of pricing American and European options by applying a physics-informed neural network (PINN) to the Black-Scholes equation, achieving accurate results on simulated data and reasonable performance on real market data compared to benchmarks.
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.