Table of Contents
Introduction (Slides 1-3)
- 1. Title Slide
- 2. Learning Objectives
- 3. Why Synthetic Financial Data?
Foundations (Slides 4-6)
- 4. Financial Time Series Properties
- 5. Fat Tails & Volatility Clustering
- 6. Generative Model Taxonomy
GANs (Slides 7-12)
- 7. GAN Architecture
- 8. GAN Training Dynamics
- 9. Mode Collapse Problem
- 10. WGAN & Gradient Penalty
- 11. TimeGAN Architecture
- 12. Conditional GANs
VAEs (Slides 13-15)
- 13. VAE Architecture
- 14. ELBO Derivation
- 15. Latent Space Visualization
Diffusion (Slides 16-20)
- 16. Diffusion Forward Process
- 17. Reverse Diffusion
- 18. SDE Formulation
- 19. GBM-Diffusion for Finance
- 20. Normalizing Flows
Private Credit (Slides 21-25)
- 21. Private Credit Overview
- 22. Loan-Level Simulation
- 23. Fund-Level Simulation
- 24. Correlation Structure
- 25. Synthetic vs Real Comparison
Evaluation (Slides 26-28)
- 26. Evaluation Metrics
- 27. Model Comparison
- 28. VaR & Stress Testing
Conclusion (Slides 29-30)
- 29. Research Frontiers
- 30. Conclusion & References
Key Mathematical Formulations
GAN Objective
minG maxD E[log D(x)] + E[log(1-D(G(z)))]
WGAN with Gradient Penalty
LGP = lambda E[(||grad D(x)||2 - 1)2]
VAE ELBO
L = Eq[log p(x|z)] - DKL(q(z|x)||p(z))
Diffusion Forward Process
q(xt|x0) = N(xt; sqrt(alphat)x0, (1-alphat)I)
Score-Based SDE
dx = f(x,t)dt + g(t)dW
Ornstein-Uhlenbeck
drt = theta(mu - rt)dt + sigma dWt
Presentation Details
| Total Slides | 30 |
| Theme | Beamer Madrid |
| Font Size | 8pt |
| Aspect Ratio | 16:9 |
| Charts Included | 24 |
| Date | January 2026 |