{"ID":2825301,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.21791","arxiv_id":"2512.21791","title":"Synthetic Financial Data Generation for Enhanced Financial Modelling","abstract":"Data scarcity and confidentiality in finance often impede model development and robust testing. This paper presents a unified multi-criteria evaluation framework for synthetic financial data and applies it to three representative generative paradigms: the statistical ARIMA-GARCH baseline, Variational Autoencoders (VAEs), and Time-series Generative Adversarial Networks (TimeGAN). Using historical S and P 500 daily data, we evaluate fidelity (Maximum Mean Discrepancy, MMD), temporal structure (autocorrelation and volatility clustering), and practical utility in downstream tasks, specifically mean-variance portfolio optimization and volatility forecasting. Empirical results indicate that ARIMA-GARCH captures linear trends and conditional volatility but fails to reproduce nonlinear dynamics; VAEs produce smooth trajectories that underestimate extreme events; and TimeGAN achieves the best trade-off between realism and temporal coherence (e.g., TimeGAN attained the lowest MMD: 1.84e-3, average over 5 seeds). Finally, we articulate practical guidelines for selecting generative models according to application needs and computational constraints. Our unified evaluation protocol and reproducible codebase aim to standardize benchmarking in synthetic financial data research.","short_abstract":"Data scarcity and confidentiality in finance often impede model development and robust testing. This paper presents a unified multi-criteria evaluation framework for synthetic financial data and applies it to three representative generative paradigms: the statistical ARIMA-GARCH baseline, Variational Autoencoders (VAEs...","url_abs":"https://arxiv.org/abs/2512.21791","url_pdf":"https://arxiv.org/pdf/2512.21791v1","authors":"[\"Christophe D. Hounwanou\",\"Yae Ulrich Gaba\",\"Pierre Ntakirutimana\"]","published":"2025-12-25T21:43:16Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"q-fin.CP\"]","methods":"[\"Generative Adversarial Network\",\"Variational Autoencoder\"]","has_code":false}