Neural Options Pricing

2021/05/27 by Timothy DeLise, DeLise, Timothy · 1 citation
Computer Science · Decision Sciences · Economics, Econometrics and Finance · #Computational Engineering #Computational Finance (q-fin.CP) #FOS: Computer and information sciences #FOS: Economics and business #Finance #Machine Learning (cs.LG) #Mathematical Finance (q-fin.MF) #Neural Networks and Applications #Stochastic processes and financial applications #Stock Market Forecasting Methods #and Science (cs.CE)

paper · pdf · doi:10.48550/arxiv.2105.13320

openalex publication_date 2021/05/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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 Itô 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.

Citations

• Stochastic Backpropagation and Approximate Inference in Deep Generative Models
• The Pricing of Options and Corporate Liabilities
• Theoretical guarantees for sampling and inference in generative models\n with latent diffusions
• Neural Stochastic Differential Equations: Deep Latent Gaussian Models in\n the Diffusion Limit
• Deep Residual Learning for Image Recognition
• Deep Neural Network Framework Based on Backward Stochastic Differential Equations for Pricing and Hedging American Options in High Dimensions

Cited by

• Efficient Calibration in the rough Bergomi model by Wasserstein distance

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