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Quantifying Distributional Model Risk via Optimal Transport

Jose Blanchet () and Karthyek Murthy ()
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Jose Blanchet: Management Science and Engineering, Stanford University, Stanford, California 94305
Karthyek Murthy: Engineering Systems and Design, Singapore University of Technology and Design, Singapore 487372

Mathematics of Operations Research, 2019, vol. 44, issue 2, 565-600

Abstract: 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 of 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. The proposed methodology is well suited for risk analysis and distributionally robust optimization, as we demonstrate with applications. We also discuss how to estimate the tolerance region nonparametrically using Skorokhod-type embeddings in some of these applications.

Keywords: model risk; distributionally robust optimization using Wasserstein distances; DRO; Kullback–Liebler divergence; ruin probabilities; diffusion approximations (search for similar items in EconPapers)
Date: 2019
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