We study the problem of ?finding the worst-case joint distribution of a set of risk factors given prescribed multivariate marginals and a nonlinear loss function. We show that when the risk measure is CVaR, and the distributions are discretized, the problem can be conveniently solved using...

An importance sampling approach for sampling copula models is introduced. We propose two algorithms that improve Monte Carlo estimators when the functional of interest depends mainly on the behaviour of the underlying random vector when at least one of the components is large. Such problems...

The standard intensity-based approach for modeling defaults is generalized by making the deterministic term structure of the survival probability stochastic via a common jump process. The survival copula of the vector of default times is derived and it is shown to be explicit and of the...

We study the problem of finding the worst-case joint distribution of a set of risk factors given prescribed multivariate marginals with nonlinear loss function. The method has applications to any situation where marginals are provided, and bounds need to be determined on total portfolio risk....

The goal of this dissertation is to explore nested Archimedean copulas. In particular, efficient sampling algorithms, especially suited for large dimensions, are presented. As an application, a pricing model for collateralized debt obligations (CDOsʺ) is developed. Copulas are distribution...

This paper presents an intellectual exchange with ChatGPT, an artificial intelligence chatbot, about correlation pitfalls in risk management. The exchange takes place in the form of a conversation that provides ChatGPT with context. The purpose of this conversation is to evaluate ChatGPT's...

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....

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...