Solutions of portfolio selection problems are often influenced by the model misspecification and simpli fications, or by errors due to approximations, estimations, and incomplete information. The obtained optimal investment strategies, recommendations for the risk and portfolio manager, should be then carefully analyzed. We shall deal with output analysis, robustness, and stress testing with respect to uncertainty or perturbations of input data for risk constrained portfolio optimization problems via the contamination technique and the worst-case analysis. We focus on problems with decision dependent random returns.
Applying the contamination techniques we present lower and upper bonds for optimal
value function for several di fferent decision dependent randomness problems.
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