这篇论文告诉你，期权价格准不代表密度恢复准。它用NIFTY和模拟数据对比了混合模型、DeepONet和SVI，结果各有胜负，适合做金融AI研究的人参考。
该论文对比了两种基准：控制基准暴露模拟器真实密度，时间排序的NIFTY基准测试市场外价格。双组分对数正态混合模型在合成基准上具有最低的聚合价格、L1、Wasserstein和固定尾部误差。DeepONet将1%分位数和方差误差分别降低39.0%和34.6%，报价变换器在结构错误设定的Merton族上将L1降低16.4%。在524个NIFTY看涨期权上，验证选择的测试时适应将DeepONet RMSE降低28.3%，但每到期混合和SVI拟合仍然更准确。证据支持目标依赖的归纳偏差，而非通用胜者。
Inverse Learning of Latent Risk-Neutral Densities from Irregular Option Quotes
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.