Reflections on robustness in stochastic programs with risk constraints Seminar

This paper is a contribution to the robustness analysis for stochastic programs whose set of feasible solutions depends on the probability distribution P. For various reasons, probability distribution P may not be precisely specified and we study robustness of results with respect to perturbations of P. The main tool is the contamination technique. For the optimal value, local contamination bounds are derived and applied to robustness analysis of the optimal value of a portfolio performance under risk-shaping constraints. To illustrate the theoretical results, numerical examples for several mean-risk models are presented. Finally, under suitable conditions on the structure of the problem and for discrete distributions we shall suggest a new robust portfolio efficiency test with respect to the first (second) order stochastic dominance criterion and we shall exploit the contamination technique to analyze the resistance with respect to additional scenarios.