MartingaleONet: Physics-Constrained Operator Learning for Real-Time Option Pricing and Volatility Calibration
A deep operator network maps volatility surfaces to option prices under the Heston model 15,000 times faster than finite-difference methods while reducing dynamic hedging variance by over 59% under transaction costs.
Featured in No. 132 on 25 Sep 2026 · 4 days after release
- Released
- 21 Sep 2026
- First featured
- No. 132 · 25 Sep 2026
- Published in
- Not yet, as far as Semantic Scholar knows
- Fanfare
- 2 of 5
- Identifier
- SSRN 7498326
- Authors
- WonChan Cho
Citations and venue from Semantic Scholar (ODC-BY), refreshed weekly. Summary: Quant Letter (CC BY 4.0).