The framework presents a method to quantify #uncertainty propagation in #dynamic #scenarios, focusing on discrete #stochastic processes over a limited time span. These dynamic uncertainty sets encompass various uncertainties like distributional ambiguity, utilizing tools like the Wasserstein distance and $f$-divergences. Dynamic robust #risk #measures, defined as maximum #risks within uncertainty sets, exhibit properties like convexity and coherence based on uncertainty set conditions. $f$-divergence-derived sets yield strong time-consistency, while Wasserstein distance leads to a new non-normalized time-consistency. Recursive representations of one-step conditional robust risk measures underlie strong or non-normalized time-consistency.
This study addresses #climate-induced decline in #honey production, a significant #risk for #beekeepers. A #parametricinsurance policy is discussed, using #weatherdata to trigger payouts for losses due to adverse conditions. The approach is evaluated using random forests, comparing beekeepers' losses to #insurance benefits under various weather #scenarios, alongside traditional methods. An #italian case example demonstrates pricing for different regions.
"The #eu proposal for the #artificialintelligenceact (#aia) defines four #risk categories: unacceptable, high, limited, and minimal. However, as these categories statically depend on broad fields of application of #ai systems (#ais), the risk magnitude may be wrongly estimated, and the AIA may not be enforced effectively. Our suggestion is to apply the four categories to the risk #scenarios of each AIs, rather than solely to its field of application."
Logistic #regressionanalysis identified significant #predictors for attitudes towards relocation and #floodinsurance decisions. The study found that low-magnitude #flood #scenarios had predictive models, while high-magnitude flooding did not. Flood insurance ownership emerged as a significant predictor for staying after flooding.
#generativeai AI offers opportunities to enhance #operationalriskmanagement #orm through its ability to analyze #unstructureddata, #simulate #risk #scenarios, and #automate tasks. However, integrating this technology into ORM presents challenges, including #dataquality, #interpretability, #performance validation, #ethicalconsiderations, and organizational readiness.
"... 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."
This paper that explores the design of #climate#stresstests to assess #macroprudential#risks from #climatechange in the #financialsector. The authors review current climate stress #scenarios employed by #regulators, highlighting the need to consider dynamic policy choices, better understand feedback loops between climate change and the economy, and explore compound #riskscenarios. They argue that more research is needed to identify channels through which plausible scenarios can impact credit risks, incorporate #bank-lending responses to #climaterisk, assess the adequacy of climate #riskpricing in #financialmarkets, and better understand the process of expectations formation around the realizations of climate risks.
"Machine learning methods are getting more and more important in the development of internal models using scenario generation. As internal models under Solvency 2 have to be validated, an important question is in which aspects the validation of these data-driven models differs from a classical theory-based model."