Neural Options Pricing
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Abstract (arXiv)
This research investigates pricing financial options based on the traditional martingale theory of arbitrage pricing applied to neural SDEs. We treat neural SDEs as universal Ito process approximators. In this way we can lift all assumptions on the form of the underlying price process, and compute theoretical option prices numerically. We propose a variation of the SDE-GAN approach by implementing the Wasserstein distance metric as a loss function for training. Furthermore, it is conjectured that the error of the option price implied by the learnt model can be bounded by the very Wasserstein distance metric that was used to fit the empirical data.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 10 of its 21 references that are in the map. First posted on arXiv 27 May 2021.
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References · 10
- 2015Deep Residual Learning for Image Recognitioncited by 51,809
- 2018Neural Ordinary Differential Equationscited by 3,733
- 2020Scalable Gradients for Stochastic Differential Equationscited by 264
- 2019Neural Stochastic Differential Equations: Deep Latent Gaussian Models in the Diffusion Limitcited by 168
- 2015On Wasserstein Two Sample Testing and Related Families of Nonparametric Testscited by 165
- 2021Neural SDEs as Infinite-Dimensional GANscited by 159
- 2019Theoretical guarantees for sampling and inference in generative models with latent diffusionscited by 97
- 2019Infinitely deep neural networks as diffusion processescited by 21
- 2019Deep Neural Network Framework Based on Backward Stochastic Differential Equations for Pricing and Hedging American Options in High Dimensionscited by 14
- 2019Pricing options and computing implied volatilities using neural networkscited by 9
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