"We study a #reinsurer who faces multiple sources of #model #uncertainty. The reinsurer offers contracts to n #insurers whose #claims follow different compound #poisson processes. As the reinsurer is uncertain about the insurers' claim severity distributions and frequencies, they design reinsurance contracts that maximise their expected wealth subject to an #entropy #penalty…."
This study employs an agent-based #model to explore how #climate shocks spread within #supplychains, linking #climateimpacts to firms' #default #risks. Integrating supply chain and financial models, it outlines a framework to simulate physical risk transmission, downstream effects, and increased default risk. Findings underscore supply chains' role in #climaterisk propagation, advocate adaptation measures, and identify vulnerable sectors. The research underscores the necessity of climate #resilience in supply chains.
Companies use #ai tools for #hr decisions, but they face a balance between benefits and #risks. With limited federal #regulation and complex state laws, employers seek guidance. The #model#riskmanagement#mrm framework, adapted from #finance, aids in managing #airisks for #employment choices. Proportionality lets employers adjust validation to risks and tech changes. Objective analysis and a competent MRM team ensure AI tools align with design and legal requirements, fostering trust and #compliance.
The authors use mid-quantile regression to deal with ordinal #riskassessments and compare their approach to current alternatives for #cyberrisk ranking and graded responses. They test their #model on both simulated and real data and discuss its applications to #threatlintelligence.
"... we find that the #bayesian approach outperforms the classical [#counterparty #risk #model] in identifying whether a model is correctly specified, which is the principal aim of any backtesting framework."
"... we propose applying the #risk categories to specific #ai #scenarios, rather than solely to fields of application, using a #riskassessment #model that integrates the #aia [#eu #aiact] with the risk approach arising from the Intergovernmental Panel on Climate Change (#ipcc) and related literature. This model enables the estimation of the magnitude of AI risk by considering the interaction between (a) risk determinants, (b) individual drivers of determinants, and (c) multiple risk types. We use large language models (#llms) as an example."
"From a #riskmanagement perspective, it is challenging to #model physical and #transitionrisks given the #uncertainty around #climaterisk drivers, such as changes in #governmentpolicy aimed at reducing #greenhousegasemissions, the pace of technological change, and uncertainty around the transmission channels. A dearth of in-house modeling tools and reliance on #thirdparty vendors also hamper #banks’ ability to properly understand and manage #risks. The most recent #boe climate biennial exploratory scenario (#cbes) noted that “banks varied in their ability to scrutinize and understand the strengths and weakness of third-party models, and adapt them appropriately to the CBES.” As a result, projected #losses for banks varied widely, suggesting a high degree of uncertainty about the magnitude of climate risks as well as a limited ability to accurately reflect such risks in business decisions."
"We address the problem of sharing risk among agents with preferences modelled by a general class of comonotonic additive and law-based functionals that need not be either monotone or convex. Such functionals are called distortion riskmetrics, which include many statistical measures of risk and variability used in portfolio optimization and insurance."
"The potential use of the proposed risk measures in insurance is illustrated by two concrete applications, capital risk allocation and premia calculation under uncertainty."