2023/12/10 by Michael Kupper, Max Nendel, Kupper, Michael +3 · 1 citation
Decision Sciences · Economics, Econometrics and Finance · Engineering · #FOS: Economics and business #Mathematical Finance (q-fin.MF) #Monetary Policy and Economic Impact #Reservoir Engineering and Simulation Methods #Risk Management (q-fin.RM) #Risk and Portfolio Optimization
paper · pdf · doi:10.48550/arxiv.2312.05973
openalex publication_date 2023/12/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this paper, we study convex risk measures with weak optimal transport penalties. In a first step, we show that these risk measures allow for an explicit representation via a nonlinear transform of the loss function. In a second step, we discuss computational aspects related to the nonlinear transform as well as approximations of the risk measures using, for example, neural networks. Our setup comprises a variety of examples, such as classical optimal transport penalties, parametric families of models, uncertainty on path spaces, moment constrains, and martingale constraints. In a last step, we show how to use the theoretical results for the numerical computation of worst-case losses in an insurance context and no-arbitrage prices of European contingent claims after quoted maturities in a model-free setting.