Neural Options Pricing

• URL: http://arxiv.org/abs/2105.13320v1
• Date: Thu, 27 May 2021 17:22:30 GMT
• Title: Neural Options Pricing
• Authors: Timothy DeLise
• Abstract summary: We treat neural SDEs as universal Ito process approximators. We compute theoretical option prices numerically. It is conjectured that the error of the option price implied by the learnt model can be bounded by the Wasserstein distance metric.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 It\^o 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.

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