发表机构
The University of Tokyo; Preferred Networks, Inc.（东京大学; Preferred Networks 公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对深度对冲中合成场景评估问题，提出以兼容性而非真实性为核心的视角，理论与实证表明对冲性能由生成器与对冲者的对齐及任务结构决定，为金融合成数据设计提供了原则性基础。
AI 中文摘要
深度对冲是一种数据驱动的学习对冲策略的方法，由于真实市场数据往往有限难以用于训练，它依赖于合成价格路径生成器。现有方法主要基于真实性评估此类生成器，即其捕捉真实市场统计特性的能力，但真实性与对冲性能之间的关系仍不明确。本研究基于兼容性概念，为深度对冲的合成数据引入了以决策为中心的视角。兼容性衡量在合成场景上训练的策略在真实市场中保持有效的程度。我们从理论上证明：1）对冲性能可分解为学习误差和兼容性差距；2）真实性与兼容性可能存在差异。实证研究发现，对冲性能并非仅由真实性决定，而是由生成器与对冲者的对齐情况以及任务结构共同决定。总体而言，本研究为设计与金融决策任务对齐的合成数据提供了原则性基础。
英文摘要
Deep hedging is a data-driven approach to learn hedging strategies. It relies on synthetic price paths generator, as real market data is often limited for training. Existing approaches primarily evaluate such generators based on realism, i.e., how well they capture statistical properties of real markets, but the relationship between realism and hedging performance remains unclear. In this work, we introduce a decision-centric perspective on synthetic data for deep hedging based on the notion of compatibility. Compatibility measures the extent to which strategies trained on synthetic scenarios remain effective in the true market. We theoretically show that 1) hedging performance decomposes into learning error and a compatibility gap, and 2) realism and compatibility can diverge. Empirically, we find that hedging performance is governed not by realism alone, but by the alignment between the generator and the hedger, together with task structure. Taken together, this work provides a principled basis for designing synthetic data in finance aligned with decision tasks.
