The ability to construct a realistic simulator of financial exchanges, including reproducing the dynamics of the limit order book, can give insight into many counterfactual scenarios, such as a flash crash, a margin call, or changes in macroeconomic outlook. In recent years, agent-based models have been developed that reproduce many features of an exchange, as summarised by a set of stylised facts and statistics. However, the ability to calibrate simulators to a specific period of trading remains an open challenge. In this work, we develop a novel approach to the calibration of market simulators by leveraging recent advances in deep learning, specifically using neural density estimators and embedding networks. We demonstrate that our approach is able to correctly identify high probability parameter sets, both when applied to synthetic and historical data, and without reliance on manually selected or weighted ensembles of stylised facts.

翻译：构建能够再现限价订单簿动态的金融市场高保真模拟器，有助于深入理解诸多反事实场景，如闪电崩盘、追加保证金通知或宏观经济预期变化。近年来，基于智能体的模型已能复现交易所的众多特征，并通过一组程式化事实与统计量进行总结。然而，如何将模拟器校准至特定交易时段仍是悬而未决的挑战。本研究利用深度学习的最新进展——具体采用神经密度估计器与嵌入网络——开发了一种新颖的市场模拟器校准方法。实验表明，无论应用于合成数据还是历史数据，该方法均能准确识别高概率参数集，且无需依赖人工选择或加权的程式化事实集合。