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动态订单流失衡与交易期限下的最优执行

文章 arXiv papers · 作者: Kyle Bechler et al.
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总结

本文提出一种最优执行模型，同时考虑瞬时价格冲击，以及与交易者影响订单流失衡有关的信息成本。订单流失衡代表市场流动的当前方向，因此模型会考虑交易者的订单与当前状况一致还是相反。模型将执行设定为平衡这些成本的连续时间随机控制问题，并允许交易期限随订单流变化而动态调整。

作者首先提出无限期限模型，然后研究依次优化价格冲击和执行期限的可解近似方法。作者报告称，这些近似方法，尤其是滚动期限版本，具有很高准确度，并且与 Almgren–Chriss 框架相关。讨论还涉及订单流的实证特征，以及与此前执行期限研究的联系。所提供的描述没有给出数据集、误差度量或具体市场状况，因此除了作者的表述外，无法评估其所称的准确度。

核心观点

  • 执行目标是在瞬时价格冲击与订单流失衡相关的信息成本之间进行权衡。
  • 模型考虑交易者的订单顺应还是逆着当前订单流。
  • 模型允许交易期限根据市场状况变化。
  • 可解近似方法依次优化价格冲击和交易期限，其中包括滚动期限方法。
  • 作者将模型与 Almgren–Chriss 联系起来，并讨论订单流的实证特征，但所提供的描述没有量化证据。

标签

交易执行市场微观结构统计学
全文
# Optimal Execution with Dynamic Order Flow Imbalance


# Optimal Execution with Dynamic Order Flow Imbalance









We examine optimal execution models that take into account both market microstructure impact and informational costs. Informational footprint is related to order flow and is represented by the trader's influence on the flow imbalance process, while microstructure influence is captured by instantaneous price impact. We propose a continuous-time stochastic control problem that balances between these two costs. Incorporating order flow imbalance leads to the consideration of the current market state and specifically whether one's orders lean with or against the prevailing order flow, key components often ignored by execution models in the literature. In particular, to react to changing order flow, we endogenize the trading horizon $T$. After developing the general indefinite-horizon formulation, we investigate several tractable approximations that sequentially optimize over price impact and over $T$. These approximations, especially a dynamic version based on receding horizon control, are shown to be very accurate and connect to the prevailing Almgren-Chriss framework. We also discuss features of empirical order flow and links between our model and "Optimal Execution Horizon" by Easley et al (Mathematical Finance, 2013).

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。