论文标题
模型不确定性：一种反向方法
Model Uncertainty: A Reverse Approach
论文作者
论文摘要
数学金融中的强大模型将经典的单概率度量取代了对世界未来状态的足够丰富的概率度量，以捕获有关未来事件的“正确”概率的不确定性。如果这套措施是非主导的，那么许多从经典主导的框架中知道的结果将不再是概率和分析工具，这对于统治模型的处理至关重要。当假设数学金融文献引起的突出结果时，我们研究了强大模型的后果。在这种情况下，我们将kreps-yan特性分类，即Brannath-Schachermayer双极定理的鲁棒变体，风险度量的FATOU表示以及可靠模型中的聚合。
Robust models in mathematical finance replace the classical single probability measure by a sufficiently rich set of probability measures on the future states of the world to capture (Knightian) uncertainty about the "right" probabilities of future events. If this set of measures is nondominated, many results known from classical dominated frameworks cease to hold as probabilistic and analytic tools crucial for the handling of dominated models fail. We investigate the consequences for the robust model when prominent results from the mathematical finance literature are postulate. In this vein, we categorise the Kreps-Yan property, robust variants of the Brannath-Schachermayer Bipolar Theorem, Fatou representations of risk measures, and aggregation in robust models.
