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Electronic Communications in Probability 15 (2010) 124-133
Measurability of optimal transportation and strong coupling of martingale measures
Joaquin Fontbona 1, Hélène Guérin 2, Sylvie Méléard 3, 4, 5
(2010)

We consider the optimal mass transportation problem in $\RR^d$ with measurably parameterized marginals, for general cost functions and under conditions ensuring the existence of a unique optimal transport map. We prove a joint measurability result for this map, with respect to the space variable and to the parameter. The proof needs to establish the measurability of some set-valued mappings, related to the support of the optimal transference plans, which we use to perform a suitable discrete approximation procedure. A motivation is the construction of a strong coupling between orthogonal martingale measures. By this we mean that, given a martingale measure, we construct in the same probability space a second one with specified covariance measure. This is done by pushing forward one martingale measure through a predictable version of the optimal transport map between the covariance measures. This coupling allows us to obtain quantitative estimates in terms of the Wasserstein distance between those covariance measures.
1:  Centro de Modelamiento Matemático (CMM)
CNRS : UMI2807 – Universidad de Chile
2:  Institut de Recherche Mathématique de Rennes (IRMAR)
CNRS : UMR6625 – Université de Rennes 1 – École normale supérieure de Cachan - ENS Cachan – Institut National des Sciences Appliquées (INSA) : - RENNES – Université de Rennes II - Haute Bretagne
3:  Centre de Mathématiques Appliquées (CMAP)
CNRS : UMR7641 – Université de Versailles Saint-Quentin-en-Yvelines – Polytechnique - X
4:  Modélisation aléatoire de Paris X (MODAL'X)
Université Paris X - Paris Ouest Nanterre La Défense
5:  Ecologie et évolution
CNRS : UMR7625 – Université Pierre et Marie Curie [UPMC] - Paris VI – Ecole normale supérieure de Paris - ENS Paris
Mathematics/Probability
Probability
Fulltext link: 
http://fr.arXiv.org/abs/0809.1111