# Distributionally Robust Martingale Optimal Transport

arXiv: https://arxiv.org/abs/2106.07191 · HTML: https://mlsanity.com/paper/2106.07191/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jun 2021.

## Built on by (most cited first, 1 of 1)

- [Distributionally robust risk evaluation with a causality constraint and structural information](https://mlsanity.com/paper/2203.10571/) (2022)

## Abstract (arXiv)

We study the problem of bounding path-dependent expectations (within any finite time horizon d) over the class of discrete-time martingales whose marginal distributions lie within a prescribed tolerance of a given collection of benchmark marginal distributions. This problem is a relaxation of the martingale optimal transport (MOT) problem and is motivated by applications to super-hedging in financial markets. We show that the empirical version of our relaxed MOT problem can be approximated within Oleft( n-1/2right) error where n is the number of samples of each of the individual marginal distributions (generated independently) and using a suitably constructed finite-dimensional linear programming problem.