Robust distortion risk measures with linear penalty under distribution uncertainty
By: Yuxin Du, Dejian Tian, Hui Zhang
Potential Business Impact:
Makes money predictions safer with uncertain numbers.
The paper investigates the robust distortion risk measure with linear penalty function under distribution uncertainty. The distribution uncertainties are characterized by predetermined moment conditions or constraints on the Wasserstein distance. The optimal quantile distribution and the optimal value function are explicitly characterized. Our results partially extend the results of Bernard, Pesenti and Vanduffel (2024) and Li (2018) to robust distortion risk measures with linear penalty. In addition, we also discuss the influence of the penalty parameter on the optimal solution.
Similar Papers
On data-driven robust distortion risk measures for non-negative risks with partial information
Risk Management
Makes money predictions safer from bad guesses.
Robust distortion risk metrics and portfolio optimization
Risk Management
Helps make better money choices when unsure.
Wasserstein Distributionally Robust Nonparametric Regression
Machine Learning (Stat)
Makes computer predictions better even with bad data.