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

Abstract

Abstract: 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 Question

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?

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Correction Record

Public corrections are additive records. Current status does not claim the synthesis is error-free.

Provenance