  The issue of model risk in default modeling has been known since inception of
the Academic literature in the field. However, a rigorous treatment requires a
description of all the possible models, and a measure of the distance between a
single model and the alternatives, consistent with the applications. This is
the purpose of the current paper. We first analytically describe all possible
joint models for default, in the class of finite sequences of exchangeable
Bernoulli random variables. We then measure how the model risk of choosing or
calibrating one of them affects the portfolio loss from default, using two
popular and economically sensible metrics, Value-at-Risk (VaR) and Expected
Shortfall (ES).
