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arXiv 1409.2618q-fin.TR
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动态订单流不平衡下的最优执行

Optimal Execution with Dynamic Order Flow Imbalance

Kyle Bechler, Mike Ludkovski

摘要:

我们研究了同时考虑市场微观结构影响和信息成本的最优执行模型。信息足迹与订单流相关,由交易者对订单流不平衡过程的影响来表示,而微观结构影响则由瞬时价格影响来表示。我们提出了一个连续时间的随机控制问题,这两个成本之间的平衡。解释订单流不平衡导致考虑当前的市场状态,特别是一个人的订单是否倾向于或反对现行的订单流,在文献中的执行模型往往忽略的关键组成部分。特别是,为了对不断变化的订单流做出反应,我们将交易范围$T$内生化。在发展了一般的不确定时域公式之后,我们研究了几个易于处理的近似,它们依次优化了价格影响和$T$。这些近似,特别是一个动态版本的基础上滚动时域控制,被证明是非常准确的,并连接到流行的Almante-Chriss框架。我们还讨论了经验订单流的特征以及我们的模型与Easley等人的“最优执行地平线”(金融数学,2013)之间的联系。

英文摘要:

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).

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