论文标题
模型不确定性下的风险度量：贝叶斯的观点
Risk measures under model uncertainty: a Bayesian viewpoint
论文作者
论文摘要
我们介绍了两种有关某种参考概率措施的风险度量，这既可以具有一定的订单结构和统治性能。当某些最小不等式实际上是平等的时候，分析它们与彼此的关系会导致问题。然后，我们提供条件，在这些条件下，可以根据一组概率措施诱导的所有风险度量在所有风险度量上定义为上限的相应稳健风险措施，可以按单一概率措施进行经典表示。我们特别关注通过使用一些先验进行混合在一组概率度量上获得的混合概率度量，例如调节器的信念。然后，就混合概率度量而言，可以将经典表示形式解释为贝叶斯的贝叶斯方法，以实现强大的风险度量。
We introduce two kinds of risk measures with respect to some reference probability measure, which both allow for a certain order structure and domination property. Analyzing their relation to each other leads to the question when a certain minimax inequality is actually an equality. We then provide conditions under which the corresponding robust risk measures, being defined as the supremum over all risk measures induced by a set of probability measures, can be represented classically in terms of one single probability measure. We focus in particular on the mixture probability measure obtained via mixing over a set of probability measures using some prior, which represents for instance the regulator's beliefs. The classical representation in terms of the mixture probability measure can then be interpreted as a Bayesian approach to robust risk measures.
