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Efficient hedging in incomplete markets under model uncertainty

We consider an investor who has sold a contingent claim and intends to minimize the maximal expected weighted shortfall. Here, the maximum is taken over a family of models and the minimum is taken over all admissible hedging strategies that satisfy a given cost constraint. We call the associated minimizing strategy robustefficient. The problem to determine a robust-efficient strategy is closely related to the statistical problem of testing a composite hypothesis against a composite alternative. The hypothesis is given by the family of pricing rules and the alternative coincides with the family of models. The mathematical centerpiece of this thesis is the solution of the statistical testing problem on a general level by means of convex duality and game-theoretical methods. The problem differs from the classical testing problem in that the power of a test is defined in terms of a strictly concave state dependent utility function rather than the identity mapping. Furthermore, our only essential assumption is that the alternative and the hypothesis are dominated, i.e., the alternative and the hypothesis need neither be parameterized nor of the form of the neighborhoods typically considered in robust statistics. Similar to the classical notion of least-favorable pairs of priordistributions on the hypothesis respectively alternative, we introduce the pivotal notion of a least-favorable pair of elements of the hypothesis respectively alternative. The main result of our analysis on maximin-optimal tests is that the maximinoptimal test can be found among the simple-optimal test for a least-favorable pair. If the least-favorable pair is equivalent to the dominating measure, the simple optimal test is the unique maximin-optimal test. If the latter condition is not fulfilled, we approximate the maximin-optimal test by a sequence of explicitly constructed simple optimal tests. These results clarify the general structure of the robust-efficient hedging strategy. We also show that a least-favorable pair can be decomposed into a worst-case model and a worst-case pricing rule for this model. The worst-case model has a very direct economic interpretation, whereas the worst-case pricing rule is a more mathematical auxiliary tool. If the worst-case model dominates all models, the efficient strategy associated to the fixed worst-case model is robust-efficient. For fixed model, the worst-case pricing rule yields the optimal modified claim and allows us to make some statements about its attainability. In the second part of this thesis, we explicitly construct the robust-efficient strategy in a series of applications. For this, the task remains to determine the efficient strategy for each fixed model and a worst-case model. First, we enlarge the family of models in order to establish existence of a worst-case model. Then we derive the dynamics of the price process, the efficient strategy and the associated risk under each (fixed) model. If the model is incomplete, we adapt the dynamic programming principle to the specific dynamics of the model to compute or approximate the efficient strategy.