TY - JOUR

T1 - Technical Note—The Joint Impact of F-Divergences and Reference Models on the Contents of Uncertainty Sets

AU - Kruse, Thomas

AU - Schneider, Judith C.

AU - Schweizer, Nikolaus

N1 - doi: 10.1287/opre.2018.1807

PY - 2019/3/1

Y1 - 2019/3/1

N2 - In the presence of model risk, it is well established to replace classical expected values with worst-case expectations over all models within a fixed radius from a given reference model. This is the “robustness” approach. For the class of F-divergences, we provide a careful assessment of how the interplay between reference model and divergence measure shapes the contents of uncertainty sets. We show that the classical divergences, relative entropy and polynomial divergences, are inadequate for reference models that are moderately heavy-tailed, such as lognormal models. Worst cases either are infinitely pessimistic or rule out the possibility of fat-tailed “power law” models as plausible alternatives. Moreover, we rule out the existence of a single F-divergence, which is appropriate regardless of the reference model. Thus, the reference model should not be neglected when settling on any particular divergence measure in the robustness approach.

AB - In the presence of model risk, it is well established to replace classical expected values with worst-case expectations over all models within a fixed radius from a given reference model. This is the “robustness” approach. For the class of F-divergences, we provide a careful assessment of how the interplay between reference model and divergence measure shapes the contents of uncertainty sets. We show that the classical divergences, relative entropy and polynomial divergences, are inadequate for reference models that are moderately heavy-tailed, such as lognormal models. Worst cases either are infinitely pessimistic or rule out the possibility of fat-tailed “power law” models as plausible alternatives. Moreover, we rule out the existence of a single F-divergence, which is appropriate regardless of the reference model. Thus, the reference model should not be neglected when settling on any particular divergence measure in the robustness approach.

KW - Management studies

KW - F-divergence

KW - heavy tails

KW - kullback-leibler divergence

KW - model risk

KW - Robustness

UR - http://www.scopus.com/inward/record.url?scp=85068468799&partnerID=8YFLogxK

U2 - 10.1287/opre.2018.1807

DO - 10.1287/opre.2018.1807

M3 - Journal articles

VL - 67

SP - 428

EP - 435

JO - Operations Research

JF - Operations Research

SN - 0030-364X

IS - 2

ER -