Deep Generative Models for Synthetic Financial Data
Analysis
This paper explores the application of deep generative models (TimeGAN and VAEs) to create synthetic financial data for portfolio construction and risk modeling. It addresses the limitations of real financial data (privacy, accessibility, reproducibility) by offering a synthetic alternative. The study's significance lies in demonstrating the potential of these models to generate realistic financial return series, validated through statistical similarity, temporal structure tests, and downstream financial tasks like portfolio optimization. The findings suggest that synthetic data can be a viable substitute for real data in financial analysis, particularly when models capture temporal dynamics, offering a privacy-preserving and cost-effective tool for research and development.
Key Takeaways
- •Deep generative models (TimeGAN and VAEs) can generate realistic synthetic financial data.
- •Synthetic data can be used as a substitute for real financial data in portfolio analysis and risk simulation.
- •TimeGAN performs well in capturing distributional shapes, volatility, and autocorrelation.
- •Synthetic data offers privacy-preserving, cost-effective, and reproducible tools for financial experimentation.
“TimeGAN produces synthetic data with distributional shapes, volatility patterns, and autocorrelation behaviour that are close to those observed in real returns.”