{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T10:54:18Z","timestamp":1791543258708,"version":"4.3.4"},"posted":{"date-parts":[[2026]]},"group-title":"SSRN","reference-count":0,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:p>Deep Hedging has been instrumental in sparking dedicated research into utilizing Neural Networks (NNs) to solve financial hedging and pricing problems that previously relied on closed-form analytical solutions. Traditional analytical frameworks rely on idealized assumptions regarding market mechanisms-particularly continuous trading, frictionless execution, and complete markets. Bridging the gap between these idealized models and real-world market frictions has been the foundational motivation behind Deep Hedging research. This paper formulates Deep Hedging as a metrics-driven, closed-loop information system spanning from initial path generation through production observability. We introduce methods for synthesizing training trajectories with controlled statistical characteristics alongside an information-theoretic measurement framework to evaluate both data richness and model efficacy across specific market regimes. Ultimately, this end-to-end approach equips trading desks with fine-grained control over the statistical distributions governing model training and provides a rigorous, experimental telemetry framework for deploying and iteratively refining Deep Hedging systems.<\/jats:p>","DOI":"10.2139\/ssrn.7584138","type":"posted-content","created":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T10:23:22Z","timestamp":1791541402000},"source":"Crossref","is-referenced-by-count":0,"title":["An Information-Theoretic and Metrics-driven Framework for Deep Hedging: From Path Generation to Production Observability"],"prefix":"10.2139","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-5763-4497","authenticated-orcid":true,"given":"Tonmoy Tapashkumar","family":"Bhattacharya","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","container-title":[],"original-title":[],"deposited":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T10:23:22Z","timestamp":1791541402000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ssrn.com\/abstract=7584138"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"references-count":0,"URL":"https:\/\/doi.org\/10.2139\/ssrn.7584138","relation":{},"subject":[],"published":{"date-parts":[[2026]]},"subtype":"preprint"}}