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Model Uncertainty: A Reverse Approach

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  • Felix-Benedikt Liebrich
  • Marco Maggis
  • Gregor Svindland

Abstract

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.

Suggested Citation

  • Felix-Benedikt Liebrich & Marco Maggis & Gregor Svindland, 2020. "Model Uncertainty: A Reverse Approach," Papers 2004.06636, arXiv.org, revised Mar 2022.
    • Handle: RePEc:arx:papers:2004.06636
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    References listed on IDEAS

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    Cited by:

    1. Shuzhen Yang & Wenqing Zhang, 2024. "Asset pricing under model uncertainty with finite time and states," Papers 2408.13048, arXiv.org.
    2. Johannes Langner & Gregor Svindland, 2022. "Bipolar Theorems for Sets of Non-negative Random Variables," Papers 2212.14259, arXiv.org, revised Nov 2024.

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