部分信息下的有毒订单流疏导
Unwinding Toxic Flow with Partial Information
AI总结：
本文针对中央交易柜台在部分信息下处理有毒订单流的问题，建立部分可观测随机控制模型，通过滤波和变分法求解最优交易策略，以最大化每日盈亏并管理库存风险。
摘要：
我们考虑一个中央交易柜台，该柜台汇总客户订单的流入，而这些订单具有未观测到的毒性，即持续的不利方向性。柜台选择以成本有效的方式将流入内部化或外部化到市场。在此模型中，外部化订单流会产生价格冲击成本，并为交易流入带来额外的市场反馈反应。柜台的目标是在考虑日终库存惩罚的情况下最大化每日交易盈亏。我们将此设定表述为一个部分可观测的随机控制问题，并分两步求解。首先，我们推导出库存和毒性的滤波动态，投影到观测滤波上，这将随机控制问题转化为一个完全可观测的问题。然后，我们使用变分方法推导出唯一的最优交易策略。我们针对柜台面临动量型和均值回归型毒性的各种情景展示了我们的结果。
英文摘要：
We consider a central trading desk which aggregates the inflow of clients' orders with unobserved toxicity, i.e. persistent adverse directionality. The desk chooses either to internalise the inflow or externalise it to the market in a cost-effective manner. In this model, externalising the order flow creates both price impact costs and an additional market feedback reaction for the inflow of trades. The desk's objective is to maximise the daily trading P&L subject to end-of-day inventory penalisation. We formulate this setting as a partially observable stochastic control problem and solve it in two steps. First, we derive the filtered dynamics of the inventory and toxicity, projected to the observed filtration, which turns the stochastic control problem into a fully observed problem. Then we use a variational approach in order to derive the unique optimal trading strategy. We illustrate our results for various scenarios in which the desk faces momentum and mean-reverting toxicity.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
