Physics Informed Neural Network for Option Pricing

The primary goal of option pricing is to work out the to the Black-Scholes equation for pricing American and European probability of whether the option is "in-the-money" or "outof-money" options. We test our approach on both simulated as when it is exercised. Option pricing is crucial well as real market data, compare it to analytical/numerical for traders, investors, and financial institutions in making benchmarks. Our model is able to accurately capture informed decisions about buying, selling, or hedging risks the price behavior on simulation data, while also exhibiting against certain underlying assets. Precise estimation of the reasonable performance for market data (with an improvement option price helps stabilize the financial market, as financial of 30% over benchmark). We also experiment with portfolios and strategies are adjusted according to the the architecture and learning process of our PINN model changes in the option price [2]. The problem of robust to provide more understanding of convergence and stability option pricing becomes even more pressing in the current issues that impact performance.