Rethinking Synthetic Scenario Realism: Compatibility, Not Fidelity, Drives Hedging Performance

Ryuji Hashimoto, Masanori Hirano, Ryota Ozaki and Kentaro Imajo

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Abstract: Deep hedging has emerged as a data-driven approach to optimizing hedging strategies. Since real market data are often limited, synthetic price paths play a crucial role in training these strategies. However, existing approaches primarily evaluate synthetic data generators based on statistical realism, while its relationship with hedging performance remains unclear. In this work, we introduce a decision-centric perspective on synthetic data for deep hedging through the notion of compatibility, which captures discrepancies in hedging risk between synthetic and real market environments. Theoretically, we establish an upper bound on hedging performance degradation in terms of learning error and a compatibility gap, and show that statistical realism and compatibility can structurally diverge. In controlled experiments, we demonstrate that the compatibility gap captures variations in hedging degradation across tasks even when generator realism is held fixed. Empirically, using real market data, we show that statistical realism does not consistently predict hedging performance, and that generator rankings depend on both hedging strategy classes and task structure.

Date: 2026-08, Revised 2026-10
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