Deep Generative Models for Synthetic Financial Data: Applications to Portfolio and Risk Modeling

Battle Count: 107

The paper is highly relevant to quantitative trading as it directly addresses portfolio construction (mean-variance optimization), risk estimation (VaR, ES via GARCH), and strategy backtesting using synthetic data. The findings demonstrate that TimeGAN-generated data can support realistic portfolio allocations and risk assessments, enabling privacy-preserving strategy development and stress-testing. However, the single-index focus and lack of intraday dynamics limit direct applicability to high-frequency or multi-asset trading strategies. The work is more foundational (validating synthetic data utility) than providing a ready-to-deploy trading system.

TimeGAN requires training multiple interconnected networks (embedding, recovery, supervised, generator, discriminator) with adversarial dynamics, making it moderately complex to implement and tune. VAE is simpler but still requires careful latent dimension selection. The downstream pipeline (GARCH estimation, mean-variance optimization, rolling-window backtesting) adds further complexity. GPU infrastructure is needed (RTX 3090, ~4.5 hours for TimeGAN). The paper provides hyperparameters and pseudo-code, reducing implementation barriers, but GAN training instability and mode collapse risks remain practical challenges.

The paper states that all code and preprocessing steps are documented and available at an accompanying GitHub repository. Random seeds were fixed to ensure deterministic results. Hardware specifications (NVIDIA RTX 3090, 64 GB RAM) and training times are provided. Hyperparameters are fully listed in Table 7. However, the specific GitHub URL is not explicitly provided in the extract, and the paper is very recent (December 2025), so external verification of reproducibility is not yet possible.