Cross-Domain Robustness of Diffusion-Generated Synthetic Financial Data in Risk Modeling Beyond Portfolio Optimization

Synthetic financial data provides a practical solution to the privacy, accessibility, and reproducibility challenges that often constrain empirical research in quantitative finance. This paper investigates the use of deep generative models, specifically Time-series Generative Adversarial Networks (TimeGAN) and Variational Autoencoders (VAEs) to generate realistic synthetic financial return series for portfolio construction and risk modeling applications. Using historical daily returns from the S and P 500 as a benchmark, we generate synthetic datasets under comparable market conditions and eva

Research goal: How do synthetic financial datasets generated by diffusion models perform in cross-domain robustness evaluations when applied to risk modeling tasks outside of portfolio optimization (e.g., stress testing, value-at-risk estimation), as compared to real financial data?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 7.8/10.