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

Yinlei Chen

doi:10.70393/6a69656173.333338

Authors

Yinlei Chen Kyungil University

DOI:

https://doi.org/10.70393/6a69656173.333338

ARK:

https://n2t.net/ark:/40704/JIEAS.v3n6a04

Disciplines:

Artificial Intelligence Technology

Subjects:

Machine Learning

References:

35

Keywords:

Generative AI, Diffusion Model, Option Pricing, Volatility Surface, U.S. Market, Financial Innovation

Abstract

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.

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Author Biography

Yinlei Chen, Kyungil University

Kyungil University, 38428, Republic of Korea.

References

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