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

OPEN ACCESS |Research Article ||3 December 2025

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

Yinlei Chen

Kyungil University

chenyinlei123@gmail.com

Kyungil University, 38428, Republic of Korea.

* Corresponding Author¹: Yinlei Chen, E-Mail: chenyinlei123@gmail.com

Publication

Accepted 2025 November 16 ; Published 2025 December 3

Journal of Industrial Engineering and Applied Science, 2025, 3(6), 3005-6071.

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.

Keywords

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

Metadata

DOI:

10.70393/6a69656173.333338

ARK:

ark:/40704/JIEAS.v3n6a04

Pages: 23-29

References: 35

Disciplines: Artificial Intelligence Technology

Subjects: Machine Learning

Cite This Article

APA Style

Chen, Y. (2025). Generative diffusion models for option pricing: a novel framework for modeling volatility dynamics in u.s. financial markets. Journal of Industrial Engineering and Applied Science, 3(6), 23-29. https://doi.org/10.70393/6a69656173.333338

Acknowledgments

The authors thank the editor and anonymous reviewers for their helpful comments and valuable suggestions.

FUNDING

Not applicable.

INSTITUTIONAL REVIEW BOARD STATEMENT

Not applicable.

DATA AVAILABILITY STATEMENT

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

INFORMED CONSENT STATEMENT

Not applicable.

CONFLICT OF INTEREST

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

AUTHOR CONTRIBUTIONS

Not applicable.

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