DYSANOS Generative Dynamic Smooth Arbitrage-free Non-parametric Option Surfaces

2026-08-12

AI Summary

This work proposes SANOS, the first generative market model capable of producing smooth, static-arbitrage-free option surfaces spanning the full range of strikes and maturities, while supporting multi-year simulation of joint dynamics for both underlying assets and option prices. Built upon an AR(1) latent-state framework, the method integrates a nonparametric surface representation with explicit static and dynamic arbitrage constraints, accompanied by a complete data pipeline and training protocol. Empirical evaluation on S&P 500 index options from 2020 to 2025 demonstrates that SANOS substantially outperforms conventional implied volatility PCA benchmarks, achieving full-dimensional surface consistency, numerical stability, and strict adherence to no-arbitrage conditions across both time and state space.

Abstract

This article presents with DYSANOS the first generative market model for smooth SANOS option surfaces for all strikes and expiries which are free of static arbitrage. Our model is designed to generate entire paths of daily spot and option prices for years in the future. We present a robust and useful if somewhat simplistic baseline hidden state generative model in the form of an AR(1) model. We discuss model setup, data pipeline, and training and investigate numerical resence of dynamic arbitrage. We illustrate model performance on Option Metrics' IvyDB S\&P Index data from 2020 to~2025 and compare it to a pure implied-vol PCA model.

Problem

• generative model
• arbitrage-free
• option surfaces
• dynamic modeling
• non-parametric

Innovation

• generative model
• arbitrage-free
• option surface
• non-parametric
• dynamic modeling