Quantifying Distributional Model Risk via Optimal Transport

Understand

This paper deals with the problem of quantifying the impact of model misspecification when computing general expected values of interest.

• The methodology that we propose is applicable in great generality, in particular, we provide examples involving path dependent expectations of stochastic processes.
• Our approach consists in computing bounds for the expectation of interest regardless of the probability measure used, as long as the measure lies within a prescribed tolerance measured in terms of a flexible class of distances from a suitable baseline model.
• These distances, based on optimal transportation between probability measures, include Wasserstein's distances as particular cases.