We address the problem of risk sharing among agents using a two-parameter class of
quantile-based risk measures, the so-called range-value-at-risk (RVaR), as their …

Reinforcement Learning (RL) provides a pathway for efficiently utilizing the battery storage
in a microgrid. However, traditional value-based RL algorithms used in battery management …

In this paper, we propose to combine the Marginal Indemnification Function (MIF)
formulation and the Lagrangian dual method to solve optimal reinsurance model with …

The robustness of risk measures to changes in underlying loss distributions (distributional
uncertainty) is of crucial importance in making well‐informed decisions. In this paper, we …

We propose a new family of risk measures, called GlueVaR, within the class of distortion risk
measures. Analytical closed‐form expressions are shown for the most frequently used …

This paper studies the set of Pareto optimal insurance contracts and the core of an
insurance game. Our setting allows multiple insurers with translation invariant preferences …

This article contains various results on a class of nonmonotone, law-invariant risk functionals
called the signed Choquet integrals. A functional characterization via comonotonic additivity …

This article investigates optimal reinsurance treaties minimizing an insurer's risk-adjusted
liability, which encompasses a risk margin quantified by distortion risk measures. Via the …

The optimal insurance problem represents a fast growing topic that explains the most
efficient contract that an insurance player may get. The classical problem investigates the …

This study develops closed-form solutions for distortion risk measures (DRM) in extreme
cases by utilizing the first two moments and the symmetry of underlying distributions. The …