模型不确定性：一种反向方法
Model Uncertainty: A Reverse Approach
摘要：
数学金融学中的稳健模型用一组足够丰富的关于世界未来状态的概率测度来取代经典的单一概率度量，以捕捉未来事件的“正确”概率的不确定性。如果这组措施是非支配的，许多结果从经典的支配框架不再举行的概率和分析工具的关键处理支配模型失败。我们调查的后果，强大的模型时，突出的结果从数学金融文献的假设。在这方面，我们分类的Kreps-严属性，强大的变体的Brannath Schachermayer双极定理，Fatou表示的风险措施，并聚集在强大的模型。
英文摘要：
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.
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