arXiv releases v3 of research on compatibility of deep hedging synthetic data
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Paper authors including Ryuji Hashimoto published v3 of a research paper on synthetic data compatibility for deep hedging. Focusing on synthetic price paths used to train deep hedging strategies, the paper proposes a decision-oriented evaluation perspective centered on compatibility, a metric that captures differences in hedging risk between synthetic and real market environments. At the theoretical level, an upper bound for hedging performance degradation is established, noting that statistical realism and compatibility may be structurally divergent. Empirical results based on real market data show that statistical realism cannot stably predict hedging performance, and generator performance rankings shift with hedging strategy category and task structure.
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- arXiv q-finRethinking Synthetic Realism: Compatibility, Not Fidelity, Drives Hedging Performance
This paper proposes a decision-oriented, compatibility-centric evaluation perspective for synthetic price paths used in deep hedging strategy training, a metric that captures differences in hedging risk between synthetic and real market environments. Theoretically, an upper bound for hedging performance degradation is established, pointing out that statistical realism and compatibility may be structurally divergent. Empirical evidence based on real market data shows that statistical realism cannot stably predict hedging performance, and the performance ranking of generators changes with the category of hedging strategy and task structure.
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