  We propose to interpret distribution model risk as sensitivity of expected
loss to changes in the risk factor distribution, and to measure the
distribution model risk of a portfolio by the maximum expected loss over a set
of plausible distributions defined in terms of some divergence from an
estimated distribution. The divergence may be relative entropy, a Bregman
distance, or an $f$-divergence. We give formulas for the calculation of
distribution model risk and explicitly determine the worst case distribution
from the set of plausible distributions. We also give formulas for the
evaluation of divergence preferences describing ambiguity averse decision
makers.
