A deep learning approach to data-driven model-free pricing and to martingale optimal transport

TL;DR

This paper presents a neural network-based method for fast, accurate computation of model-free derivative price bounds and optimal hedging strategies, applicable to high-dimensional financial data and martingale optimal transport problems.

Contribution

It introduces a novel supervised learning approach that trains a single neural network offline for quick online pricing and hedging of diverse financial derivatives.

Findings

High accuracy demonstrated on real market data

Fast computation of price bounds for high-dimensional derivatives

Effective solution to martingale optimal transport problems

Abstract

We introduce a novel and highly tractable supervised learning approach based on neural networks that can be applied for the computation of model-free price bounds of, potentially high-dimensional, financial derivatives and for the determination of optimal hedging strategies attaining these bounds. In particular, our methodology allows to train a single neural network offline and then to use it online for the fast determination of model-free price bounds of a whole class of financial derivatives with current market data. We show the applicability of this approach and highlight its accuracy in several examples involving real market data. Further, we show how a neural network can be trained to solve martingale optimal transport problems involving fixed marginal distributions instead of financial market data.