A Toolkit for Robust Risk Assessment UsingF-Divergences
构建了一个工具包，用于在模型不确定性由F-散度球定义时评估模型风险，提出了易于实现且灵活的新F-散度族，并应用于运营管理、保险和金融中的风险管理问题。
This paper assembles a toolkit for the assessment of model risk when model uncertainty sets are defined in terms of an F-divergence ball around a reference model. We propose a new family of F-divergences that are easy to implement and flexible enough to imply convincing uncertainty sets for broad classes of reference models. We use our theoretical results to construct concrete examples of divergences that allow for significant amounts of uncertainty about lognormal or heavy-tailed Weibull reference models without implying that the worst case is necessarily infinitely bad. We implement our tools in an open-source software package and apply them to three risk management problems from operations management, insurance, and finance. This paper was accepted by Baris Ata, stochastic models and simulation.