Risk measuring under model uncertainty
By Jocelyne Bion-Nadal, Magali Kervarec. 2010
53 citations recorded by the source corpus.
The framework of this paper is that of risk measuring under uncertainty, which is when no reference probability measure is given. To every regular convex risk measure on ${\cal C}_b(\Omega)$, we associate a unique equivalence class of probability measures on Borel sets, characterizing the riskless non positive elements of ${\cal C}_b(\Omega)$. We prove that the convex risk measure has a dual representation with a countable set of probability measures absolutely continuous with respect to a certain probability measure in this class. To get these results we study the topological properties of the dual of the Banach space $L^1(c)$ associated to a capacity $c$. As application we obtain that every $G$-expectation $\E$ has a representation with a countable set of probability measures absolutely continuous with respect to a probability measure $P$ such that $P(|f|)=0$ iff $\E(|f|)=0$. We also apply our results to the case of uncertain volatility.
View Paper: https://www.semanticscholar.org/paper/b43cccf0088a6ed280f10eb42d939f4276435d70
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Citations in the corpus
- MULTIVARIATE RISK MEASURES: A CONSTRUCTIVE APPROACH BASED ON SELECTIONS (2013, background): “ht not be interpreted as the family of selections of a set-valued portfolio. This may be of advantage in the dynamic setting (Feinstein and Rudloff, 2014a) or for considering uncertainty models as in (Bion-Nadal and Kervarec, 2012). 3.2 Coherency of selection risk measure Definition 3.3. The selection”