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通过库存管理优化随机订单流的平仓

文章 arXiv papers · 作者: Marcel Nutz et al.
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总结

本文探讨在未来订单流不确定时，中心化交易台应如何处理流入订单。交易台可以暂存订单，希望用之后的反向订单流抵消，也可以将订单发送到市场并承担价差和价格冲击相关成本。作者针对一类一般的流入过程建立了决策模型，并给出可通过数值方法实现的半闭式解析解。

由于策略会根据预期未来流入量进行调整，因此不同于已知订单规模时使用的标准执行方式。调整取决于订单流自相关；文中称，只有在鞅式（即如实反映信息的）订单流下，短视平仓才是最优的。模拟涵盖多种应用场景和市场状态，作者还提出了实用指标。所提供的文本未给出模拟参数或定量成本比较，因此无法据此确认该方法在特定交易台或订单流环境下的表现。

核心观点

  • 中心风险交易台可以暂存流入交易，以便与之后的反向订单抵消，也可以将其转至市场。
  • 决策模型考虑了价差和价格冲击等交易成本。
  • 预期未来流入量会使策略相较于预先已知订单规模时采用的策略有所调整。
  • 调整取决于订单流自相关，短视平仓仅适用于鞅式订单流。
  • 作者提供了半闭式解和模拟，但描述未给出定量比较。

标签

交易执行市场微观结构风险管理统计学套利
全文
# Unwinding Stochastic Order Flow: When to Warehouse Trades


# Unwinding Stochastic Order Flow: When to Warehouse Trades









We study how to unwind stochastic order flow with minimal transaction costs. Stochastic order flow arises, e.g., in the central risk book (CRB), a centralized trading desk that aggregates order flows within a financial institution. The desk can warehouse in-flow orders, ideally netting them against subsequent opposite orders (internalization), or route them to the market (externalization) and incur costs related to price impact and bid-ask spread. We model and solve this problem for a general class of in-flow processes, enabling us to study in detail how in-flow characteristics affect optimal strategy and core trading metrics. Our model allows for an analytic solution in semi-closed form and is readily implementable numerically. Compared with a standard execution problem where the order size is known upfront, the unwind strategy exhibits an additive adjustment for projected future in-flows. Its sign depends on the autocorrelation of orders; only truth-telling (martingale) flow is unwound myopically. In addition to analytic results, we present extensive simulations for different use cases and regimes, and introduce new metrics of practical interest.

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。