arXiv releases research on risk-neutral density inversion from irregular option quotes
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Four researchers including Lennon J. Shikhman released version v3 of a paper on arXiv on inverting latent risk-neutral densities from irregular option quotes. The paper was published at ICAIF 2026, and the study notes that accurate option prices do not imply accurate recovery of the underlying risk-neutral density. The study found through comparison between synthetic simulation benchmarks and NIFTY option out-of-sample benchmarks that the DeepONet model reduced the 1% quantile error by 39.0% and the variance error by 34.6% relative to the hybrid model; on the structurally misspecified Merton family, the quote Transformer reduced the L1 error by 16.4%; after applying quality and forward constraints, 95 of the 126 pricing directions were numerically zero; test-time adaptation selected via validation set reduced the RMSE of DeepONet by 28.3%.
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- arXiv q-finResearch on Inferring Implied Risk-Neutral Density from Irregular Option Quotes
Published at ICAIF 2026, this study finds that accurate option prices do not necessarily mean accurate recovery of the underlying risk-neutral density. By comparing synthetic simulation benchmarks with out-of-sample NIFTY option benchmarks, the research reveals that the two-component lognormal mixture model demonstrates overall pricing performance on synthetic benchmarks.
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