Whack-a-mole Online Learning: Physics-Informed Neural Network for Intraday Implied Volatility Surface¶
Bibliographic record. Follow the original-source link for the publication.
| Field | Value |
|---|---|
| Primary domain | Volatility |
| Other domains | — |
| Methods | Financial Ml |
| Facets | — |
| Authors | Kentaro Hoshisashi, Carolyn E. Phelan, Paolo Barucca |
| Published | 2024-11-04 |
| Source | arXiv Quantitative Finance History |
| Identifiers | arxiv:2411.02375 |
| URL | Open original source |
Editorial synthesis¶
Why it matters¶
Under sparse intraday IV data, a method that enforces PDE and no-arbitrage constraints in real-time could reduce calibration latency and consistency risk, which is highly relevant for operational risk control. (abstract:S1, abstract:S2, abstract:S3, abstract:S4, abstract:S5, abstract:S6)
Main author claims¶
- The authors claim WamOL introduces self-adaptive balancing of losses to enforce PDE and no-arbitrage constraints while fitting IV surfaces. (
abstract:S3,abstract:S4) - They claim superior intraday IVS calibration from sparse data and improved capture of dynamic option-price/risk profile evolution. (
abstract:S1,abstract:S5,abstract:S6)
Data, method, or discussion scope¶
The evidence scope is limited to abstract-level method framing and claimed experiments; no implementation details, windowing scheme, numerical stability checks, or benchmark numbers are given. (abstract:S1, abstract:S2, abstract:S3, abstract:S4, abstract:S5, abstract:S6)
Main limitations¶
The claim of superior performance lacks explicit metrics and comparator names. In production, opaque adaptive weighting could over-smooth or become unstable around market extremes. (abstract:S4, abstract:S5, abstract:S2)
Relationships¶
- None recorded.