After a brief overview of aspects of computational risk management, the implementation of the rearrangement algorithm in R is considered as an example from computational risk management practice. This algorithm is used to compute the largest quantile (worst value-at-risk) of the sum of the...

The purpose and novelty of this article is to investigate the extent to which artificial intelligence chatbot ChatGPT can grasp concepts from quantitative risk management. To this end, we enter a scholarly discussion with ChatGPT in the form of questions and answers, and analyze the responses....

Grouped normal variance mixtures are a class of multivariate distributions that generalize classical normal variance mixtures such as the multivariate t distribution, by allowing different groups to have different (comonotone) mixing distributions. This allows one to better model risk factors...

Necessary and sufficient conditions for the subadditivity of Value-at-Risk (V aRα) for portfolios of bonds are presented under various dependence assumptions. For sufficiently large α, V aRα is subadditive. However, for any α one can construct portfolios for which V aRα is superadditive.

In this paper, we propose a novel framework for estimating systemic risk measures and risk allocations based on Markov Chain Monte Carlo (MCMC) methods. We consider a class of allocations whose jth component can be written as some risk measure of the jth conditional marginal loss distribution...