Adaptively trained Physics-informed Radial Basis Function Neural Networks for Solving Multi-asset Option Pricing Problems
arXiv:2601.12704v2 Announce Type: replace Abstract: The present study investigates the numerical solution of Black-Scholes partial differential equation (PDE) for option valuation with multiple underlying assets. We develop a physics-informed (PI) machine learning algorithm based on a radial basis function neural network (RBFNN) that concurrently optimizes the network architecture and predicts the target option price. The physics-informed radial basis function neural network (PIRBFNN) combines the strengths of the traditional radial basis function collocation method and the physics-informed ne
The continuous advancements in AI and machine learning techniques are increasingly being applied to complex financial modeling problems, pushing the boundaries of traditional numerical methods.
Sophisticated readers should care because this development indicates a growing capability for AI to handle complex, real-time financial derivative pricing, potentially leading to more efficient markets and new arbitrage opportunities.
The use of physics-informed neural networks offers a more efficient and accurate method for multi-asset option pricing compared to traditional PDEs, reducing computational overhead and improving model robustness.
- · Quantitative hedge funds
- · Financial institutions with large derivatives portfolios
- · AI/ML model developers
- · High-frequency trading firms
- · Traditional quantitative analysts
- · Firms reliant on legacy pricing models
- · Low-computational-power financial entities
More accurate and rapid pricing of complex financial derivatives.
Increased adoption of AI and machine learning across broader financial risk management and trading strategies.
Potential for new financial products and market structures enabled by superior pricing and hedging capabilities.
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Read at arXiv cs.LG