pith:M7ZM724C
Differential Machine Learning for 0DTE Options with Stochastic Volatility and Jumps
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.
arxiv:2603.07600 v4 · 2026-03-08 · q-fin.CP
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Record completeness
Claims
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.
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.
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.
Receipt and verification
| First computed | 2026-06-09T02:07:23.351847Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
67f2cfeb8254354e68dc86fd205bcb6851ff5832fec39cc7e0aa33e16a753127
Aliases
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Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/M7ZM724CKQ2U42G4Q36SAW6LNB \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 67f2cfeb8254354e68dc86fd205bcb6851ff5832fec39cc7e0aa33e16a753127
Canonical record JSON
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