Convex Hedging in Incomplete Markets

TL;DR

This paper investigates optimal hedging strategies in incomplete markets using convex risk measures, deriving conditions for static optimization problems and revealing a randomized test structure for the solution.

Contribution

It introduces necessary and sufficient optimality conditions for convex risk minimization in static hedging, highlighting a $0$-$1$-structure in the solution.

Findings

Optimal strategies involve superhedging a modified claim.

Static optimization reduces to a randomized test problem.

Solution exhibits a typical $0$-$1$-structure.

Abstract

In incomplete financial markets not every contingent claim can be replicated by a self-financing strategy. The risk of the resulting shortfall can be measured by convex risk measures, recently introduced by F\"ollmer, Schied (2002). The dynamic optimization problem of finding a self-financing strategy that minimizes the convex risk of the shortfall can be split into a static optimization problem and a representation problem. It follows that the optimal strategy consists in superhedging the modified claim $\widetilde{\varphi}H$, where $H$ is the payoff of the claim and $\widetilde{\varphi}$ is the solution of the static optimization problem, the optimal randomized test. In this paper, we will deduce necessary and sufficient optimality conditions for the static problem using convex duality methods. The solution of the static optimization problem turns out to be a randomized test with a typical $0$-$1$-structure.