Option-implied rep advances
A new arXiv paper titled 'Shallow Representation of Option Implied Information' proposes compact representations of option-implied signals that could simplify derivatives-pricing workflows and feature engineering. That technical advance is immediately relevant for building scalable option-based factors. (x.com)
Jimin Lin (Quantitative Research, Bloomberg) submitted "Shallow Representation of Option Implied Information" to arXiv as arXiv:2603.17151 on 17 March 2026. (arxiv.org) Proposition 3.1 in the paper provides an explicit equation that expresses the implied risk‑neutral density as a pointwise transformation of a Black‑Scholes quasi‑density, treating implied volatility as a numerical corrector. (arxiv.org) The proposed neural representation embeds that differentiable corrector so static‑arbitrage constraints on implied density become explicit constraints on the network representation of implied volatility. (arxiv.org) Empirical work in the manuscript uses an additive logistic synthetic benchmark and finds that deeper or wider neural architectures do not necessarily improve fit, while a shallow feedforward network with a single hidden layer and a specific activation effectively approximates both implied density and implied volatility. (arxiv.org) The arXiv entry classifies the work under Computational Finance and Machine Learning, reports a submission package size of 2,071 KB, and notes a DataCite DOI via arXiv pending registration. (arxiv.org) Aggregators mirror the submission and show a "Request Code" flag on the paper listing, indicating code is not bundled with the arXiv PDF and must be requested or hosted separately. (catalyzex.com)
