arXiv paper: Inverse Learning of Latent Risk-Neutral Densities from Irregular Option Quotes

arXiv ID: 2607.27188v1 Title: Inverse Learning of Latent Risk-Neutral Densities from Irregular Option Quotes Authors: Lennon J. Shikhman, Michael Galarnyk, Aadi Dash, Nicholas A. Welsh Primary category: cs.LG Categories: cs.LG, q-fin.CP, q-fin.PR, q-fin.ST Comment: 7 pages, 4 figures, 2 tables. Submitted to the 7th ACM International Conference on AI in Finance (ICAIF 2026) Published: 2026-07-29T17:56:20Z Updated: 2026-07-29T17:56:20Z Abstract: Accurate option prices do not imply accurate recovery of the latent risk-neutral density. We study this distinction with two complementary benchmarks. A controlled benchmark exposes simulator-truth densities for latent evaluation, while a chronological NIFTY benchmark tests only held-out market prices. A two-component lognormal mixture has the lowest aggregate price, $L^1$, Wasserstein, and fixed-tail errors on the synthetic benchmark. Learned operators retain narrower strengths: DeepONet reduces 1% quantile and variance error by 39.0% and 34.6% relative to the mixture, and a quote transformer reduces $L^1$ by 16.4% on the structurally misspecified Merton family. A numerical conditioning analysis explains why these rankings can differ: after enforcing mass and forward constraints, 95 of 126 pricing directions are numerically null, and two densities separated by $L^1 = 0.061$ produce identical prices on the covered strikes. On 524 held-out NIFTY calls, validation-selected test-time adaptation reduces DeepONet RMSE by 28.3%, but per-expiry mixture and SVI fits remain much more accurate. The evidence supports target-dependent inductive bias, not a universal winner. PDF: https://arxiv.org/pdf/2607.27188v1