Causal optimal transport and its links to enlargement of filtrations and continuous-time stochastic optimization

TL;DR

This paper introduces causal transport plans in the context of filtration enlargements, linking optimal transport theory with stochastic process decompositions to analyze information effects on stochastic optimization.

Contribution

It establishes a novel connection between causal transport plans and semimartingale decompositions under filtration enlargements, providing new tools for stochastic optimization analysis.

Findings

Characterization of when a Brownian motion remains a semimartingale after filtration enlargement.

Development of robust transport-based estimates for additional information value.

Extension of results to multidimensional continuous semimartingales.

Abstract

The martingale part in the semimartingale decomposition of a Brownian motion with respect to an enlargement of its filtration, is an anticipative mapping of the given Brownian motion. In analogy to optimal transport theory, we define causal transport plans in the context of enlargement of filtrations, as the Kantorovich counterparts of the aforementioned non-adapted mappings. We provide a necessary and sufficient condition for a Brownian motion to remain a semimartingale in an enlarged filtration, in terms of certain minimization problems over sets of causal transport plans. The latter are also used in order to give robust transport-based estimates for the value of having additional information, as well as model sensitivity with respect to the reference measure, for the classical stochastic optimization problems of utility maximization and optimal stopping. Our results have natural extensions to the case of general multidimensional continuous semimartingales.