Technical Note-The Joint Impact of F-Divergences and Reference Models on the Contents of Uncertainty Sets
成果类型：
Article
署名作者：
Kruse, Thomas; Schneider, Judith C.; Schweizer, Nikolaus
署名单位：
University of Duisburg Essen; University of Munster; Tilburg University
刊物名称：
OPERATIONS RESEARCH
ISSN/ISSBN：
0030-364X
DOI：
10.1287/opre.2018.1807
发表日期：
2019
页码：
428-435
关键词：
摘要：
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.