{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:KYKJGYIPU54A6ABWYCU6WJF5UP","short_pith_number":"pith:KYKJGYIP","schema_version":"1.0","canonical_sha256":"561493610fa7780f0036c0a9eb24bda3da5f27ce517baad358854286b8b6b96f","source":{"kind":"arxiv","id":"2509.05911","version":1},"attestation_state":"computed","paper":{"title":"Deep Learning Option Pricing with Market Implied Volatility Surfaces","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"q-fin.CP","authors_text":"Egang Lu, Kin Cheung, Lijie Ding","submitted_at":"2025-09-07T04:03:48Z","abstract_excerpt":"We present a deep learning framework for pricing options based on market-implied volatility surfaces. Using end-of-day S\\&P 500 index options quotes from 2018-2023, we construct arbitrage-free volatility surfaces and generate training data for American puts and arithmetic Asian options using QuantLib. To address the high dimensionality of volatility surfaces, we employ a variational autoencoder (VAE) that compresses volatility surfaces across maturities and strikes into a 10-dimensional latent representation. We feed these latent variables, combined with option-specific inputs such as strike a"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2509.05911","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-fin.CP","submitted_at":"2025-09-07T04:03:48Z","cross_cats_sorted":[],"title_canon_sha256":"9c2a678a90eed27ee707775fdc3fdbea916ae287f08994b96311fd06c9fc7633","abstract_canon_sha256":"0f879ba33bafbc86e196e766fb1012aa46d2ba432a1892031ceb6ec8a7b4077f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:06:26.355942Z","signature_b64":"PqTQ4bFokGYJ/1Tqj6BjXKMLNMgKMCrrvHvsmp/tZ/vtCPOm2GGNNmxqtgTX2+kENXyCsIz0H3NSSE22kAukCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"561493610fa7780f0036c0a9eb24bda3da5f27ce517baad358854286b8b6b96f","last_reissued_at":"2026-07-05T12:06:26.355449Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:06:26.355449Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deep Learning Option Pricing with Market Implied Volatility Surfaces","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"q-fin.CP","authors_text":"Egang Lu, Kin Cheung, Lijie Ding","submitted_at":"2025-09-07T04:03:48Z","abstract_excerpt":"We present a deep learning framework for pricing options based on market-implied volatility surfaces. Using end-of-day S\\&P 500 index options quotes from 2018-2023, we construct arbitrage-free volatility surfaces and generate training data for American puts and arithmetic Asian options using QuantLib. To address the high dimensionality of volatility surfaces, we employ a variational autoencoder (VAE) that compresses volatility surfaces across maturities and strikes into a 10-dimensional latent representation. We feed these latent variables, combined with option-specific inputs such as strike a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2509.05911","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2509.05911/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2509.05911","created_at":"2026-07-05T12:06:26.355504+00:00"},{"alias_kind":"arxiv_version","alias_value":"2509.05911v1","created_at":"2026-07-05T12:06:26.355504+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2509.05911","created_at":"2026-07-05T12:06:26.355504+00:00"},{"alias_kind":"pith_short_12","alias_value":"KYKJGYIPU54A","created_at":"2026-07-05T12:06:26.355504+00:00"},{"alias_kind":"pith_short_16","alias_value":"KYKJGYIPU54A6ABW","created_at":"2026-07-05T12:06:26.355504+00:00"},{"alias_kind":"pith_short_8","alias_value":"KYKJGYIP","created_at":"2026-07-05T12:06:26.355504+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/KYKJGYIPU54A6ABWYCU6WJF5UP","json":"https://pith.science/pith/KYKJGYIPU54A6ABWYCU6WJF5UP.json","graph_json":"https://pith.science/api/pith-number/KYKJGYIPU54A6ABWYCU6WJF5UP/graph.json","events_json":"https://pith.science/api/pith-number/KYKJGYIPU54A6ABWYCU6WJF5UP/events.json","paper":"https://pith.science/paper/KYKJGYIP"},"agent_actions":{"view_html":"https://pith.science/pith/KYKJGYIPU54A6ABWYCU6WJF5UP","download_json":"https://pith.science/pith/KYKJGYIPU54A6ABWYCU6WJF5UP.json","view_paper":"https://pith.science/paper/KYKJGYIP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2509.05911&json=true","fetch_graph":"https://pith.science/api/pith-number/KYKJGYIPU54A6ABWYCU6WJF5UP/graph.json","fetch_events":"https://pith.science/api/pith-number/KYKJGYIPU54A6ABWYCU6WJF5UP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KYKJGYIPU54A6ABWYCU6WJF5UP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KYKJGYIPU54A6ABWYCU6WJF5UP/action/storage_attestation","attest_author":"https://pith.science/pith/KYKJGYIPU54A6ABWYCU6WJF5UP/action/author_attestation","sign_citation":"https://pith.science/pith/KYKJGYIPU54A6ABWYCU6WJF5UP/action/citation_signature","submit_replication":"https://pith.science/pith/KYKJGYIPU54A6ABWYCU6WJF5UP/action/replication_record"}},"created_at":"2026-07-05T12:06:26.355504+00:00","updated_at":"2026-07-05T12:06:26.355504+00:00"}