mlsanityData to 7 Oct 2026

FuNVol: A Multi-Asset Implied Volatility Market Simulator using Functional Principal Components and Neural SDEs

arXiv 2303.00859 ↗First posted 1 Mar 2023q-fin.CP cs.LG q-fin.STcited by 6

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Abstract (arXiv)

We introduce a new approach for generating sequences of implied volatility (IV) surfaces across multiple assets that is faithful to historical prices. We do so using a combination of functional data analysis and neural stochastic differential equations (SDEs) combined with a probability integral transform penalty to reduce model misspecification. We demonstrate that learning the joint dynamics of IV surfaces and prices produces market scenarios that are consistent with historical features and lie within the sub-manifold of surfaces that are essentially free of static arbitrage. Finally, we demonstrate that delta hedging using the simulated surfaces generates profit and loss (P&L) distributions that are consistent with realised P&Ls.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 18 of its 46 references that are in the map. First posted on arXiv 1 Mar 2023.

Builds on

18 of 46 references.

References · 18

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  8. 2021Deep Hedging of Derivatives Using Reinforcement Learningcited by 30
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6 more
  1. 2019Deep Smoothing of the Implied Volatility Surfacecited by 19
  2. 2021Arbitrage-free neural-SDE market modelscited by 18
  3. 2021Arbitrage-Free Implied Volatility Surface Generation with Variational Autoencoderscited by 9
  4. 2022Conditionally Elicitable Dynamic Risk Measures for Deep Reinforcement Learningcited by 9
  5. 2019Incorporating prior financial domain knowledge into neural networks for implied volatility surface predictioncited by 6
  6. 2017Online Adaptive Machine Learning Based Algorithm for Implied Volatility Surface Modelingcited by 4

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Cited by year

Citing papers per year: 2023: 2; 2024: 2; 2025: 2.

6 papers.

  1. 2023Non-adversarial training of Neural SDEs with signature kernel scorescited by 36
  2. 2023Generative modeling for time series via Schr"odinger bridgecited by 18
  3. 2024Efficient Training of Neural Stochastic Differential Equations by Matching Finite Dimensional Distributionscited by 4
  4. 2025Universal approximation property of neural stochastic differential equationscited by 2
  5. 2025Efficient Neural SDE Training using Wiener-Space Cubaturecited by 1
  6. 2024Kullback-Leibler Barycentre of Stochastic Processescited by 0