Seminar by Silvana Pesenti

Dynamic robust risk measures with applications

This talk is focused on distributionally robust risk measures, which are the largest value a risk measure can attain within an uncertainty set.

Uncertainty sets are often characterised by balls around a reference distribution, thus containing plausible alternative distributions. I first discuss worst-case distortion risk measures for Wasserstein uncertainty sets and an application to portfolio optimisation.

Second, I discuss the dynamic setting, where the risk of stochastic processes is evaluated using time-consistent dynamic risk measures. In the dynamic setting I introduce dynamic robust risk measures and dynamic uncertainty sets and study conditions on the uncertainty sets that lead to well-known properties of dynamic robust risk measures, such as convexity and coherence.

Furthermore, we proof necessary and sufficient properties for a robust dynamic risk measures to be time-consistent and to admit a recursive representation.