Synthetic Financial Data Generation for Enhanced Financial Modelling
Paper#Financial Modeling, Synthetic Data, Generative Models🔬 Research|Analyzed: Jan 4, 2026 00:05•
Published: Dec 25, 2025 21:43
•1 min read
•ArXivAnalysis
This paper addresses the critical problem of data scarcity and confidentiality in finance by proposing a unified framework for evaluating synthetic financial data generation. It compares three generative models (ARIMA-GARCH, VAEs, and TimeGAN) using a multi-criteria evaluation, including fidelity, temporal structure, and downstream task performance. The research is significant because it provides a standardized benchmarking approach and practical guidelines for selecting generative models, which can accelerate model development and testing in the financial domain.
Key Takeaways
- •Addresses data scarcity and confidentiality issues in finance.
- •Proposes a unified multi-criteria evaluation framework for synthetic financial data.
- •Compares ARIMA-GARCH, VAEs, and TimeGAN generative models.
- •TimeGAN shows the best balance between realism and temporal coherence.
- •Provides practical guidelines for model selection based on application needs.
Reference / Citation
View Original"TimeGAN achieved the best trade-off between realism and temporal coherence (e.g., TimeGAN attained the lowest MMD: 1.84e-3, average over 5 seeds)."