Optimal Unwinding of Stochastic Order Flow Through Warehousing

Summary

This work examines how a centralized trading desk should handle incoming orders when future order flow is uncertain. The desk can warehouse orders in hopes of offsetting them against later opposite-side flow, or send them to the market and pay costs associated with spread and price impact. The authors formulate the choice for a general class of inflow processes and provide an analytic solution in semi-closed form that can also be implemented numerically.

The optimal strategy differs from standard execution with a known order size because it adjusts for projected future inflows. The adjustment depends on order-flow autocorrelation; the document says myopic unwinding is optimal only for martingale, or truth-telling, flow. Simulations cover multiple use cases and regimes, and the authors introduce practical metrics. The supplied text gives no simulation parameters or quantitative cost comparisons, so it does not establish performance for a particular desk or flow environment.

Key ideas

• A central risk desk can warehouse incoming trades to net them against later opposite-side orders or externalize them to market.
• The decision model accounts for transaction costs, including spread and price impact.
• Projected future inflows create an adjustment to the strategy used when order size is known upfront.
• The adjustment depends on order-flow autocorrelation, and myopic unwinding applies only to martingale flow.
• The authors provide a semi-closed-form solution and simulations, but the description gives no quantitative comparisons.

Tags

# 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.

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