Evaluating generative models for synthetic financial data

Battle Count: 80

Highly relevant for quantitative trading as it addresses synthetic data generation for backtesting, risk model validation, and stress testing without exposing proprietary data. The downstream evaluation on portfolio optimization (Sharpe ratio, allocation similarity) and volatility forecasting directly connects to trading strategy development. The privacy leakage analysis is critical for firms handling sensitive trading data. However, the paper focuses on evaluation methodology rather than proposing new trading strategies, and the single-asset limitation reduces direct applicability to multi-asset portfolio construction.

ARIMA-GARCH is straightforward to implement (<0.1 hrs training). VAE requires moderate deep learning expertise (1.5 hrs training). TimeGAN is the most complex, requiring GRU-based encoder/decoder/generator/discriminator/supervisor architecture with two-phase training (4.5 hrs), careful hyperparameter tuning, and sensitivity to mode collapse. The evaluation framework itself (MMD, KS, PCA, spectral analysis, downstream tasks, privacy tests) adds additional implementation overhead. Overall, reproducing the full pipeline requires familiarity with both econometric and deep learning frameworks.

The paper provides detailed hyperparameters in Appendix A, specifies 5 random seeds for all experiments, describes preprocessing steps explicitly, reports hardware specifications (NVIDIA RTX 3090, 64GB RAM), and states that a reproducible codebase is publicly released. However, the exact GitHub URL is not provided in the extract. The experimental protocol is well-documented with train/validation/test splits, sequence construction details, and evaluation procedures.