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

Paper: arXiv 2512.21798

Authors: Christophe D. Hounwanou, Yae Ulrich Gaba

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 evaluate them using statistical similarity metrics, temporal structure tests, and downstream financial tasks. The study shows that TimeGAN produces synthetic data with distributional shapes, volatility patterns, and autocorrelation behaviour that are close to those observed in real returns. When applied to mean–variance portfolio optimization, the resulting synthetic datasets lead to portfolio weights, Sharpe ratios, and risk levels that remain close to those obtained from real data. The VAE provides more stable training but tends to smooth extreme market movements, which affects risk estimation. Finally, the analysis supports the use of synthetic datasets as substitutes for real financial data in portfolio analysis and risk simulation, particularly when models are able to capture temporal dynamics. Synthetic data therefore provides a privacy-preserving, cost-effective, and reproducible tool for financial experimentation and model development.

Complexity vs Empirical Score

• Math Complexity: 6.5/10
• Empirical Rigor: 6.0/10
• Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper introduces formal mathematical frameworks for portfolio optimization and generative models, but focuses on empirical evaluation using real financial data (S&P 500) with statistical tests and downstream tasks, resulting in moderate scores in both dimensions.

Research Flowchart

  flowchart TD
  A["Research Goal:<br/>Generate realistic synthetic financial data<br/>for portfolio and risk modeling"] --> B["Data Used:<br/>Historical S&P 500 Daily Returns"]
  B --> C{"Computational Process:<br/>Deep Generative Models"}
  C --> D["Time-series GAN<br/>TimeGAN"]
  C --> E["Variational Autoencoder<br/>VAE"]
  D --> F["Downstream Evaluation"]
  E --> F
  F --> G["Key Findings:<br/>TimeGAN captures volatility & correlations<br/>VAE smooths extreme movements<br/>Synthetic data viable for financial tasks"]

Cited References (19)

1. Deep learning in finance (2017) — link
2. Empirical asset pricing via machine learning (2020)
3. A survey of synthetic data generation methods for privacy preservation and data augmentation (2022)
4. Analysis of financial time series (2010)
5. Generative adversarial nets (2014)
6. Auto-encoding variational Bayes (2014) — link
7. Time-series generative adversarial networks (2019)
8. Data augmentation using generative adversarial networks for time series classification (2019)

1 of 19 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.