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高频交易如何加剧波动并促成闪崩

文章 arXiv papers · 作者: Sandrine Jacob Leal et al.
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总结

本文构建了一个基于智能体的模型，研究低频和高频交易者之间的互动及其对资产价格的影响。低频智能体按时间顺序交易，并可在基本面分析规则与图表分析规则之间切换。高频智能体会对价格事件作出反应，并使用方向性策略利用较慢交易者产生的信息。研究通过蒙特卡洛模拟评估波动性和闪崩行为，并称该模型能够再现若干重要的市场典型事实。

在模拟中，加入高频交易者会提高波动性，并助长闪崩形成。作者提出的机制是买卖价差扩大，以及限价订单簿中的同步抛售。更频繁的撤单会提高闪崩发生频率，同时缩短其持续时间。这些结果取决于模型设计；摘录没有提供校准细节、实证验证，也没有证据表明模拟机制能够解释现实中的特定闪崩。

核心观点

  • 低频智能体遵循基于时间的规则，并可在基本面分析和图表分析行为之间切换。
  • 高频智能体会对价格波动作出反应，并进行方向性交易。
  • 模拟将高频交易与更高波动性和闪崩形成联系起来。
  • 作者指出，价差扩大和同步抛售是闪崩背后的机制。
  • 更高的撤单率会增加闪崩发生次数，同时缩短闪崩持续时间。

标签

高频交易市场微观结构统计学交易执行
全文
# Rock around the Clock: An Agent-Based Model of Low- and High-Frequency Trading


# Rock around the Clock: An Agent-Based Model of Low- and High-Frequency Trading









We build an agent-based model to study how the interplay between low- and high-frequency trading affects asset price dynamics. Our main goal is to investigate whether high-frequency trading exacerbates market volatility and generates flash crashes. In the model, low-frequency agents adopt trading rules based on chronological time and can switch between fundamentalist and chartist strategies. On the contrary, high-frequency traders activation is event-driven and depends on price fluctuations. High-frequency traders use directional strategies to exploit market information produced by low-frequency traders. Monte-Carlo simulations reveal that the model replicates the main stylized facts of financial markets. Furthermore, we find that the presence of high-frequency trading increases market volatility and plays a fundamental role in the generation of flash crashes. The emergence of flash crashes is explained by two salient characteristics of high-frequency traders, i.e. their ability to i) generate high bid-ask spreads and ii) synchronize on the sell side of the limit order book. Finally, we find that higher rates of order cancellation by high-frequency traders increase the incidence of flash crashes but reduce their duration.

在遵守原作品许可的前提下，附作者信息全文展示。 许可协议: abstract CC0

此摘要由 Stratmill 研究智能体根据原文撰写，并非原文副本。