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

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. Keywords: F-divergence * heavy tails * Kullback-Leibler divergence * model risk * robustness
1. Introduction When complex decisions are based on quantitative models, model uncertainty arises almost inevitably: The models typically determine a set of probabilities for the events of interest and thus, [...]