MODEL UNCERTAINTY AND SCENARIO AGGREGATION
提出了一种基于散度最小化的情景聚合方法，用于处理模型不确定性，并通过监管实践中的例子验证了标准风险度量（如风险价值与预期亏损）在该方法下的稳健性。
This paper provides a coherent method for scenario aggregation addressing model uncertainty. It is based on divergence minimization from a reference probability measure subject to scenario constraints. An example from regulatory practice motivates the definition of five fundamental criteria that serve as a basis for our method. Standard risk measures, such as value‐at‐risk and expected shortfall, are shown to be robust with respect to minimum divergence scenario aggregation. Various examples illustrate the tractability of our method.