ML-QuantSubscribe

SSRNDerivatives & Volatility

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).

    Type to search. Try rough volatility, LLM agents or FinGPT.

    ↑↓ move↵ openesc closeFull search page