Robust optimized certainty equivalents and quantiles for loss positions with distribution uncertainty

Abstract

The paper investigates the robust optimized certainty equivalents and analyzes the relevant properties of them as risk measures for loss positions with distribution uncertainty. On this basis, the robust generalized quantiles are proposed and discussed. The robust expectiles with two specific penalization functions $\varphi_{1}$ and $\varphi_{2}$ are further considered respectively. The robust expectiles with $\varphi_{1}$ are proved to be coherent risk measures, and the dual representation theorems are established. In addition, the effect of penalization functions on the robust expectiles and its comparison with expectiles are examined and simulated numerically.