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Arbitrage-Free Neural-SDE Market Models

Author

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  • Samuel N. Cohen
  • Christoph Reisinger
  • Sheng Wang

Abstract

Modelling joint dynamics of liquid vanilla options is crucial for arbitrage-free pricing of illiquid derivatives and managing risks of option trade books. This paper develops a nonparametric model for the European options book respecting underlying financial constraints and while being practically implementable. We derive a state space for prices which are free from static (or model-independent) arbitrage and study the inference problem where a model is learnt from discrete time series data of stock and option prices. We use neural networks as function approximators for the drift and diffusion of the modelled SDE system, and impose constraints on the neural nets such that no-arbitrage conditions are preserved. In particular, we give methods to calibrate neural SDE models which are guaranteed to satisfy a set of linear inequalities. We validate our approach with numerical experiments using data generated from a Heston stochastic local volatility model.

Suggested Citation

  • Samuel N. Cohen & Christoph Reisinger & Sheng Wang, 2023. "Arbitrage-Free Neural-SDE Market Models," Applied Mathematical Finance, Taylor & Francis Journals, vol. 30(1), pages 1-46, January.
    • Handle: RePEc:taf:apmtfi:v:30:y:2023:i:1:p:1-46
    DOI: 10.1080/1350486X.2023.2257217
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    Cited by:

    1. Francesca Biagini & Lukas Gonon & Niklas Walter, 2024. "Universal randomised signatures for generative time series modelling," Papers 2406.10214, arXiv.org, revised Sep 2024.

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