Quantifying Distributional Model Risk via Optimal Transport
Jose Blanchet () and
Karthyek Murthy ()
Additional contact information
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
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)
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3) Track citations by RSS feed
Downloads: (external link)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:inm:ormoor:v:44:y:2019:i:2:p:565-600
Access Statistics for this article
More articles in Mathematics of Operations Research from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Matthew Walls ().