评测生成式AI对订单簿数据的模拟效果，发现自回归模型优于传统方法。
LOB-Bench: Benchmarking Generative AI for Finance -- an Application to Limit Order Book Data
- 构建基于Python的订单簿生成数据评估框架，支持多变量统计分析。
- 自回归生成模型在分布相似性与市场影响模拟上超越GAN和参数化模型。
- 适合金融建模、生成式AI研究者用于验证订单簿数据真实性。
由于金融数据具有高噪声、厚尾分布及策略交互等特性，序列建模挑战极大，但缺乏统一的量化评估标准。为此，我们提出LOB-Bench，一个基于Python的基准测试框架，用于评估以LOBSTER格式生成的限价订单簿（LOB）消息级数据的质量与真实性。该框架通过比较生成数据与真实数据在条件与无条件统计量上的分布差异进行评估，支持灵活的多变量统计分析。包含常用订单簿指标如买卖价差、订单簿总量、订单不平衡度、消息到达时间间隔，以及训练好的判别器得分。此外，还集成“市场冲击指标”，即特定事件下的交叉相关性和价格响应函数。我们在生成式自回归状态空间模型、（C）GAN及参数化订单簿模型上进行了基准测试，结果表明自回归生成式AI方法在性能上优于传统模型类别。
原文摘要 · Abstract (English)
While financial data presents one of the most challenging and interesting sequence modelling tasks due to high noise, heavy tails, and strategic interactions, progress in this area has been hindered by the lack of consensus on quantitative evaluation paradigms. To address this, we present LOB-Bench, a benchmark, implemented in python, designed to evaluate the quality and realism of generative message-by-order data for limit order books (LOB) in the LOBSTER format. Our framework measures distributional differences in conditional and unconditional statistics between generated and real LOB data, supporting flexible multivariate statistical evaluation. The benchmark also includes features commonly used LOB statistics such as spread, order book volumes, order imbalance, and message inter-arrival times, along with scores from a trained discriminator network. Lastly, LOB-Bench contains "market impact metrics", i.e. the cross-correlations and price response functions for specific events in the data. We benchmark generative autoregressive state-space models, a (C)GAN, as well as a parametric LOB model and find that the autoregressive GenAI approach beats traditional model classes.
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