GROMM: Generative Risk-neutral Option Market Models
Abstract
We present a statically and dynamically arbitrage-free European option market model with smooth interpolation between option prices, based on the SANOS framework, and prove that its dynamics admit a unique strong solution. In spirit, it is a stochastic model for local volatility when the latter is used as a representation of a European option price surface, not as a pricing model. We illustrate the strength of our approach on S&P Index Option data from January 2022 to August 2025. We show that the model's initial state fits within bid/ask for more than 95% of relevant market option prices. We then train the model dynamics under ℙ and ℚ and show that the model is indeed free of arbitrage while fitting the dynamics of the market well. We also propose a range of BHW-tests to test for absence of arbitrage.
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BibTeX
@misc{wiese2026gromm,
title = {{GROMM: Generative Risk-neutral Option Market Models}},
author = {Magnus Wiese and Hans Buehler and Blanka Horvath},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.16732},
eprint = {2610.16732},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.16732},
}
RIS
TY - UNPB
TI - GROMM: Generative Risk-neutral Option Market Models
AU - Magnus Wiese
AU - Hans Buehler
AU - Blanka Horvath
PY - 2026
DA - 2026/10/21
PB - arXiv
DO - 10.48550/arxiv.2610.16732
UR - https://arxiv.org/abs/2610.16732
AB - We present a statically and dynamically arbitrage-free European option market model with smooth interpolation between option prices, based on the SANOS framework, and prove that its dynamics admit a unique strong solution. In spirit, it is a stochastic model for local volatility when the latter is used as a representation of a European option price surface, not as a pricing model. We illustrate the strength of our approach on S&P Index Option data from January 2022 to August 2025. We show that the model's initial state fits within bid/ask for more than 95% of relevant market option prices. We then train the model dynamics under ℙ and ℚ and show that the model is indeed free of arbitrage while fitting the dynamics of the market well. We also propose a range of BHW-tests to test for absence of arbitrage.
ER -