Deep Generative Models for Quantitative Finance
This course introduces deep generative models and their applications in quantitative finance, focusing on their ability to generate realistic, high-dimensional market data where traditional parametric models often fail—particularly in the tails and in data-limited stress scenarios. Emphasis is placed on combining modern generative AI with core financial mathematics, ensuring consistency with probabilistic foundations, stochastic processes, and no-arbitrage principles.
The course reviews essential probability, asset-pricing, and deep-learning concepts before covering the main generative model families—generative adversarial networks, normalizing flows, and diffusion/score-based models—and the practical challenges of training, conditioning, and evaluation in financial settings. These tools are applied to key problems including return and scenario generation, portfolio construction, risk-neutral density estimation, derivatives/surrogate pricing and calibration, stress testing and risk management, and market microstructure and order-book simulation.
By the end, students will be able to design and assess deep generative models as financially consistent predictors, simulators, and surrogates for modern quantitative finance applications.
The course reviews essential probability, asset-pricing, and deep-learning concepts before covering the main generative model families—generative adversarial networks, normalizing flows, and diffusion/score-based models—and the practical challenges of training, conditioning, and evaluation in financial settings. These tools are applied to key problems including return and scenario generation, portfolio construction, risk-neutral density estimation, derivatives/surrogate pricing and calibration, stress testing and risk management, and market microstructure and order-book simulation.
By the end, students will be able to design and assess deep generative models as financially consistent predictors, simulators, and surrogates for modern quantitative finance applications.
Lecturer
Date
27th February ~ 26th June, 2026
Location
| Weekday | Time | Venue | Online | ID | Password |
|---|---|---|---|---|---|
| Friday | 09:50 - 12:15 | Shuangqing-B627 | ZOOM 06 | 537 192 5549 | BIMSA |
Audience
Advanced Undergraduate
, Graduate
Video Public
Yes
Notes Public
Yes
Language
Chinese
, English
Lecturer Intro
I have been an Associate Professor at BIMSA since 2025. Prior to this role, I was an Assistant Professor at the Institute of Statistics and Big Data, Renmin University of China. My research lies at the intersection of AI and finance/business, focusing on FinTech and Business Analytics through innovative machine learning and data science methodologies. My interests include tail risk management, empirical asset pricing, portfolio optimization, derivatives, consumer credit, and related areas. Recently, I have also developed an interest in out-of-distribution (OOD) generalization and uncertainty quantification in machine learning. I publish in both finance/business and machine learning academic journals and conferences.
I am seeking highly self-motivated Postdoctoral researchers or research interns to conduct high-quality research in the areas of AI, digital economy, or applied mathematics. If you are interested, please feel free to contact me.
I am seeking highly self-motivated Postdoctoral researchers or research interns to conduct high-quality research in the areas of AI, digital economy, or applied mathematics. If you are interested, please feel free to contact me.