AP-PEN: A Hard-Constrained, Physics-Enforced Network for Convenience-Yield Inversion from Commodity Futures
A neural network that analytically enforces the Gibson-Schwartz PDE recovers commodity convenience yield from futures and avoided implausible estimates during the April 2020 WTI dislocation.
Featured in No. 138 on 6 Nov 2026 · 3 days after release
- Released
- 3 Nov 2026
- First featured
- No. 138 · 6 Nov 2026
- Published in
- Not yet, as far as Semantic Scholar knows
- Fanfare
- 2 of 5
- Identifier
- SSRN 7677598
- Authors
- Evan Lynch
Citations and venue from Semantic Scholar (ODC-BY), refreshed weekly. Summary: Quant Letter (CC BY 4.0).