{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:JDM5QHO4NKDEBBZQ2W6WPNJ7QW","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"43a22f6768bcb398c5b562c6ad2a56280fbade000b486c05bddcf3eb7e923d1c","cross_cats_sorted":["cs.LG","math.PR","q-fin.MF"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"q-fin.CP","submitted_at":"2024-03-01T18:46:26Z","title_canon_sha256":"cb11de4d899e8792fe7bbcd6c93897c9f92692d024572656add663a6f2b51c1e"},"schema_version":"1.0","source":{"id":"2403.00746","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2403.00746","created_at":"2026-07-05T10:43:35Z"},{"alias_kind":"arxiv_version","alias_value":"2403.00746v2","created_at":"2026-07-05T10:43:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.00746","created_at":"2026-07-05T10:43:35Z"},{"alias_kind":"pith_short_12","alias_value":"JDM5QHO4NKDE","created_at":"2026-07-05T10:43:35Z"},{"alias_kind":"pith_short_16","alias_value":"JDM5QHO4NKDEBBZQ","created_at":"2026-07-05T10:43:35Z"},{"alias_kind":"pith_short_8","alias_value":"JDM5QHO4","created_at":"2026-07-05T10:43:35Z"}],"graph_snapshots":[{"event_id":"sha256:7e1352222180cb442fe0e43fbbd04c45c26fe6199d3bed77af796232c003d6bc","target":"graph","created_at":"2026-07-05T10:43:35Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2403.00746/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We develop a novel deep learning approach for pricing European options in diffusion models, that can efficiently handle high-dimensional problems resulting from Markovian approximations of rough volatility models. The option pricing partial differential equation is reformulated as an energy minimization problem, which is approximated in a time-stepping fashion by deep artificial neural networks. The proposed scheme respects the asymptotic behavior of option prices for large levels of moneyness, and adheres to a priori known bounds for option prices. The accuracy and efficiency of the proposed ","authors_text":"Antonis Papapantoleon, Jasper Rou","cross_cats":["cs.LG","math.PR","q-fin.MF"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"q-fin.CP","submitted_at":"2024-03-01T18:46:26Z","title":"A time-stepping deep gradient flow method for option pricing in (rough) diffusion models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.00746","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:f02bfcb7073799d945f8fcc2b2ab9f66bba107bf0d7e1b89ca809fc9cd8fd0b0","target":"record","created_at":"2026-07-05T10:43:35Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"43a22f6768bcb398c5b562c6ad2a56280fbade000b486c05bddcf3eb7e923d1c","cross_cats_sorted":["cs.LG","math.PR","q-fin.MF"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"q-fin.CP","submitted_at":"2024-03-01T18:46:26Z","title_canon_sha256":"cb11de4d899e8792fe7bbcd6c93897c9f92692d024572656add663a6f2b51c1e"},"schema_version":"1.0","source":{"id":"2403.00746","kind":"arxiv","version":2}},"canonical_sha256":"48d9d81ddc6a86408730d5bd67b53f85a9386454c8c39a3da0340eed7f6fff63","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"48d9d81ddc6a86408730d5bd67b53f85a9386454c8c39a3da0340eed7f6fff63","first_computed_at":"2026-07-05T10:43:35.118978Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:43:35.118978Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Q3z60K6vw2pTN9e97/e+8Ad6IbLu88zDygBEYO8nptWLBviCEtqo9HsXfrdKQC2wiTOBWN7Aa9IjeNQXxj+LCg==","signature_status":"signed_v1","signed_at":"2026-07-05T10:43:35.119431Z","signed_message":"canonical_sha256_bytes"},"source_id":"2403.00746","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f02bfcb7073799d945f8fcc2b2ab9f66bba107bf0d7e1b89ca809fc9cd8fd0b0","sha256:7e1352222180cb442fe0e43fbbd04c45c26fe6199d3bed77af796232c003d6bc"],"state_sha256":"2911f3b9868153b1f285ba943eb6cce791b33f45e8dc56166419138513cb808b"}