论文标题
高频金融市场模拟和闪存崩溃方案分析：基于代理的建模方法
High-frequency financial market simulation and flash crash scenarios analysis: an agent-based modelling approach
论文作者
论文摘要
本文介绍了基于代理的建模框架中Flash崩溃方案的模拟和分析。我们设计，实施和评估一个基于高频代理的新型金融市场模拟器，该模拟器为E Mini S＆P 500期货市场生成了现实的毫秒级财务价格时间序列。具体而言，提供了在中央限制订单书上交易的单个安全性的微观结构模型，其中不同类型的交易者遵循不同的行为规则。使用机器学习替代建模方法对模型进行校准。统计测试和力矩覆盖率的结果表明，该模型具有在金融市场中重现现实风格化事实的出色能力。通过介绍一个模仿现实世界卖算法的机构交易员，2010年5月6日，拟议的基于高频代理的金融市场模拟器被用来模拟当天发生的闪存崩溃。我们在模拟闪光灯崩溃期间仔细检查了市场动态，并表明模拟动态与历史闪存崩溃场景中发生的情况一致。在蒙特卡洛模拟的帮助下，我们发现了模拟2010 Flash崩溃的幅度与三个条件之间的功能关系：卖出算法的数量百分比，做市商库存限制和基本交易者的交易频率。对迷你闪存事件进行了类似的分析。该模型介绍了创新的“尖峰交易者”，目的是促使迷你闪存事件。我们在典型的模拟迷你闪存事件过程中分析市场动态，并研究影响其特征的条件。所提出的模型可用于测试交易算法的弹性和鲁棒性，并为决策者提供建议。
This paper describes simulations and analysis of flash crash scenarios in an agent-based modelling framework. We design, implement, and assess a novel high-frequency agent-based financial market simulator that generates realistic millisecond-level financial price time series for the E-Mini S&P 500 futures market. Specifically, a microstructure model of a single security traded on a central limit order book is provided, where different types of traders follow different behavioural rules. The model is calibrated using the machine learning surrogate modelling approach. Statistical test and moment coverage ratio results show that the model has excellent capability of reproducing realistic stylised facts in financial markets. By introducing an institutional trader that mimics the real-world Sell Algorithm on May 6th, 2010, the proposed high-frequency agent-based financial market simulator is used to simulate the Flash Crash that took place that day. We scrutinise the market dynamics during the simulated flash crash and show that the simulated dynamics are consistent with what happened in historical flash crash scenarios. With the help of Monte Carlo simulations, we discover functional relationships between the amplitude of the simulated 2010 Flash Crash and three conditions: the percentage of volume of the Sell Algorithm, the market maker inventory limit, and the trading frequency of fundamental traders. Similar analyses are carried out for mini flash crash events. An innovative "Spiking Trader" is introduced to the model, aiming at precipitating mini flash crash events. We analyse the market dynamics during the course of a typical simulated mini flash crash event and study the conditions affecting its characteristics. The proposed model can be used for testing resiliency and robustness of trading algorithms and providing advice for policymakers.
