  The theory of convex risk functions has now been well established as the
basis for identifying the families of risk functions that should be used in
risk averse optimization problems. Despite its theoretical appeal, the
implementation of a convex risk function remains difficult, as there is little
guidance regarding how a convex risk function should be chosen so that it also
well represents one's own risk preferences. In this paper, we address this
issue through the lens of inverse optimization. Specifically, given solution
data from some (forward) risk-averse optimization problems we develop an
inverse optimization framework that generates a risk function that renders the
solutions optimal for the forward problems. The framework incorporates the
well-known properties of convex risk functions, namely, monotonicity,
convexity, translation invariance, and law invariance, as the general
information about candidate risk functions, and also the feedbacks from
individuals, which include an initial estimate of the risk function and
pairwise comparisons among random losses, as the more specific information. Our
framework is particularly novel in that unlike classical inverse optimization,
no parametric assumption is made about the risk function, i.e. it is
non-parametric. We show how the resulting inverse optimization problems can be
reformulated as convex programs and are polynomially solvable if the
corresponding forward problems are polynomially solvable. We illustrate the
imputed risk functions in a portfolio selection problem and demonstrate their
practical value using real-life data.
