{"paper":{"title":"Differential Machine Learning for 0DTE Options with Stochastic Volatility and Jumps","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"A three-stage differential machine learning procedure approximates jump terms more accurately in ultra-short maturity options while preserving pricing accuracy and delivering faster Greeks than Fourier methods.","cross_cats":[],"primary_cat":"q-fin.CP","authors_text":"Takayuki Sakuma","submitted_at":"2026-03-08T12:10:24Z","abstract_excerpt":"We present a differential machine learning method for zero-days-to-expiry (0DTE) options under a stochastic-volatility jump-diffusion model. To handle the ultra-short-maturity regime, we express the option price in Black-Scholes form with a maturity-gated variance correction, combining supervision on prices and Greeks with a PIDE-residual penalty. Prices and Greeks are derived from a single trained pricing network, while jump-term identifiability is ensured by a jump-operator network fitted jointly in a three-stage procedure. The method improves jump-term approximation relative to one-stage ba"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"The method improves jump-term approximation relative to one-stage baselines while maintaining comparable pricing errors. Furthermore, it reduces errors in Greeks, produces stable one-day delta hedges, and offers significant speedups over Fourier-based benchmarks.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That the three-stage joint training of pricing and jump-operator networks, together with the maturity-gated variance correction, reliably identifies jump terms and produces accurate Greeks for ultra-short maturities without overfitting to the specific training data or model assumptions.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"A three-stage differential ML method prices 0DTE options under SVJD by combining Black-Scholes with maturity-gated variance correction, joint pricing and jump-operator networks, and PIDE-residual penalties to improve jump approximation and Greeks while speeding up computation.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"A three-stage differential machine learning procedure approximates jump terms more accurately in ultra-short maturity options while preserving pricing accuracy and delivering faster Greeks than Fourier methods.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"45ebb2b1bbc677e2f4668afc6e37d4e2af8880c955735bb759ca84336d0f009f"},"source":{"id":"2603.07600","kind":"arxiv","version":4},"verdict":{"id":"a56ac7a9-b345-4c3d-829c-7d1cd571f30e","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-15T14:55:45.002559Z","strongest_claim":"The method improves jump-term approximation relative to one-stage baselines while maintaining comparable pricing errors. Furthermore, it reduces errors in Greeks, produces stable one-day delta hedges, and offers significant speedups over Fourier-based benchmarks.","one_line_summary":"A three-stage differential ML method prices 0DTE options under SVJD by combining Black-Scholes with maturity-gated variance correction, joint pricing and jump-operator networks, and PIDE-residual penalties to improve jump approximation and Greeks while speeding up computation.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That the three-stage joint training of pricing and jump-operator networks, together with the maturity-gated variance correction, reliably identifies jump terms and produces accurate Greeks for ultra-short maturities without overfitting to the specific training data or model assumptions.","pith_extraction_headline":"A three-stage differential machine learning procedure approximates jump terms more accurately in ultra-short maturity options while preserving pricing accuracy and delivering faster Greeks than Fourier methods."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2603.07600/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"}