Risk measuring under model uncertainty

TL;DR

This paper develops a framework for risk measurement under model uncertainty without a fixed probability measure, providing dual representations and applications to G-expectations and uncertain volatility.

Contribution

It introduces a novel dual representation of convex risk measures under model uncertainty using countable sets of probability measures.

Findings

Dual representation with countable probability measures

Characterization of riskless elements via equivalence classes

Application to G-expectations and uncertain volatility

Abstract

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.