Generative Diffusion Models for Option Pricing: A Novel Framework for Modeling Volatility Dynamics in U.S. Financial Markets

This study proposes a generative diffusion modeling framework to estimate option prices and volatility surfaces in U.S. financial markets. Unlike conventional stochastic volatility models, the diffusion model learns the data-generating process directly from historical option chains and market images. The approach converts price trajectories into “market images” and employs conditional diffusion to generate realistic future states, enabling robust and data-driven option valuation. The method demonstrates superior accuracy under extreme market conditions, providing valuable insights for U.S. risk management and derivative policy design. This research contributes to the national interest by advancing AI-driven financial modeling and supporting the technological edge of U.S. quantitative finance.