Alignment of GAN- and VAE-Generated Synthetic Financial Data in Multimodal Risk Modeling Performance

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 does the alignment of synthetic financial data generated by GANs versus VAEs influence the downstream performance of multimodal models in risk modeling tasks, as measured by F1-score on TabBench adversarial robustness tests?

Autonomous synthesis report generated by SOVEREIGN Research Kernel. Tribunal consensus score: 7.5/10.