Volatility Surface Reconstruction using Deep Learning under No-Arbitrage Constraints¶
Bibliographic record. Follow the original-source link for the publication.
| Field | Value |
|---|---|
| Primary domain | Volatility |
| Other domains | — |
| Methods | Financial Ml |
| Facets | — |
| Authors | Pablo Rodriguez Manzi |
| Published | 2026-05-20 |
| Source | arXiv Quantitative Finance History |
| Identifiers | arxiv:2605.24031 |
| URL | Open original source |
Editorial synthesis¶
Why it matters¶
This study reconstructs implied-volatility surfaces from sparse noisy quotes with no-arbitrage constraints, which is important for quote-clean environments and model selection under incomplete markets. (abstract:S1, abstract:S2, abstract:S3, abstract:S4)
Main author claims¶
- The authors claim to reconstruct implied-volatility surfaces under no-arbitrage constraints from sparse and noisy quotes and benchmark multiple network architectures against classical SVI parameterizations. (
abstract:S1,abstract:S2) - They further claim Transformer and U-Net perform strongly, especially in sparse regimes, and that soft arbitrage penalties reduce arbitrage violations with moderate effect on reconstruction error. (
abstract:S3,abstract:S4)
Data, method, or discussion scope¶
The scope includes sparse-noisy quote reconstruction, architecture set, sparse-regime performance claims, and explicit accuracy versus arbitrage-consistency trade-off analysis. (abstract:S1, abstract:S2, abstract:S3, abstract:S4)
Main limitations¶
Exact regularization grids, compute costs, and out-of-domain error bounds are absent, as are operational metrics for risk and latency. (abstract:S3, abstract:S4)
Relationships¶
- None recorded.