发表机构
Simudyne（Simudyne）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文扩展Almgren-Chriss框架，引入跨期现金约束，将多资产最优执行问题转化为可凸化的QCQP，并通过实验证明该方法能降低峰值现金回撤且保持实现缺口。
AI 中文摘要
多资产环境中的最优执行（OE）涉及跨资产的复杂交互，特别是在投资组合再平衡期间通过共享资本约束产生的交互。现有模型虽能捕捉交叉影响和投资组合层面的动态，但在很大程度上忽略了执行轨迹中显式现金约束的作用。因此，在有限资本下执行策略的可行性仍鲜有研究。本文扩展了经典的Almgren-Chriss框架，纳入对预期现金消耗的跨期约束，要求在每个交易时段，预期累计现金支出不超过预设预算。我们证明，由此产生的多资产OE问题可等价地表述为二次约束二次规划（QCQP），并进一步证明在温和条件下其具有凸表示。这为分析动态资本约束下的执行策略提供了可处理的框架。通过受控的合成实验，我们表明随着约束收紧，所提出的现金约束会定性改变OE计划，转向现金可行的先卖后执行。此外，在样本外的基于智能体的市场模拟器中的评估表明，我们的方法大幅降低了峰值现金回撤，同时保持了与现有执行策略相当的实现缺口。我们的结果强调了在多资产执行中显式建模财务可行性的重要性，并为弥合理论OE模型与实际资本约束之间的鸿沟奠定了基础。
英文摘要
Optimal execution (OE) in multi-asset settings involves complex interactions across assets, particularly through shared capital constraints during portfolio rebalancing. While existing models capture cross-impact and portfolio-level dynamics, they largely overlook the role of explicit cash constraints along the execution trajectory. As a result, the feasibility of execution strategies under limited capital remains poorly understood. In this paper, we extend the classical Almgren-Chriss framework to incorporate intertemporal constraints on expected cash consumption, requiring that the expected cumulative cash spent does not exceed a prescribed budget at every trading period. We show that the resulting multi-asset OE problem can be equivalently formulated as a quadratically constrained quadratic program (QCQP), and further establish that it admits a convex representation under mild conditions. This provides a tractable framework for analyzing execution strategies under dynamic capital constraints. Through controlled synthetic experiments, we show that the proposed cash constraints qualitatively alter OE schedules toward cash-feasible sell-first executions as the constraints become tighter. Furthermore, evaluations in an out-of-sample agent-based market simulator demonstrate that our method substantially reduces peak cash drawdown while maintaining implementation shortfall comparable to existing execution strategies. Our results highlight the importance of explicitly modeling financial feasibility in multi-asset execution and provide a foundation for bridging theoretical OE models with practical capital constraints.
Comments8 pages, 4 figures, 4 tables
