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.

翻译：金融领域的数据稀缺性与保密性常阻碍模型开发与稳健性测试。本文提出一个统一的多准则评估框架用于合成金融数据，并将其应用于三种代表性生成范式：统计ARIMA-GARCH基线模型、变分自编码器（VAEs）以及时间序列生成对抗网络（TimeGAN）。基于标普500指数历史日度数据，我们从保真度（最大平均差异，MMD）、时序结构（自相关性与波动率聚集）及下游任务实际效用（具体包括均值-方差投资组合优化与波动率预测）三个维度进行评估。实证结果表明：ARIMA-GARCH能捕捉线性趋势与条件波动率，但无法复现非线性动态特征；VAEs生成的平滑轨迹会低估极端事件；而TimeGAN在真实性与时序连贯性之间取得了最佳平衡（例如TimeGAN获得最低MMD值：1.84e-3，5次随机种子平均值）。最后，我们根据应用需求与计算约束提出了生成模型选择的实践指南。本研究的统一评估框架与可复现代码库旨在为合成金融数据研究建立标准化基准。