A Geometry-Aware Residual Correction of Hagan's SABR Implied Volatility Formula¶
Bibliographic record. Follow the original-source link for the publication.
| Field | Value |
|---|---|
| Primary domain | Volatility |
| Other domains | — |
| Methods | Financial Ml, Research Methods |
| Facets | — |
| Authors | Adil Reghai, Lama Tarsissi, Gérard Biau, Alex Lipton |
| Published | 2026-05-07 |
| Source | arXiv Quantitative Finance History |
| Identifiers | arxiv:2605.06604 |
| URL | Open original source |
Editorial synthesis¶
Why it matters¶
The approach keeps a structured SABR backbone and learns residual corrections, potentially balancing interpretability and speed for calibration systems if residual behavior is validated across regimes. (abstract:S1, abstract:S2, abstract:S3, abstract:S6, abstract:S7, abstract:S8, abstract:S9)
Main author claims¶
- The authors claim a hybrid method that augments neural inputs with SABR-geometric features and trains the network to learn residual error versus Hagan’s approximation rather than raw implied volatility. (
abstract:S1,abstract:S2,abstract:S5,abstract:S6) - They further claim improved accuracy and robustness over analytical and standard neural approaches under realistic and stressed regimes, while remaining lightweight and structurally consistent for real-time pricing and calibration. (
abstract:S7,abstract:S8,abstract:S9)
Data, method, or discussion scope¶
Scope includes the hybrid design, residual-learning target, experimental claims in regular and stressed settings, and practical calibration suitability; specific metrics, baselines, and runtime benchmarks are absent. (abstract:S1, abstract:S2, abstract:S3, abstract:S6, abstract:S7, abstract:S8, abstract:S9)
Main limitations¶
No explicit error metrics or stress-scenario definitions are provided for the reported gains, limiting tolerance analysis under calibration perturbations. (abstract:S8, abstract:S9)
Relationships¶
- None recorded.