This research evaluates different regression models to predict #flood-induced #insuranceclaims, using the #us #national #floodinsurance Program (#nfip) dataset from 2000 to 2020. The models studied include #neuralnetworks (Conditional Generative Adversarial Networks), #decisiontrees (Extreme Gradient Boosting), and #kernel-based regressors (#gaussian Process). The study identifies key predictors for regression, highlighting factors that influence flood-related financial damages.
The theory of regulatory compliance has enabled the development of differential monitoring, emphasizing tailored, impactful regulations over uniform approaches, proving crucial in risk assessment and
This paper defines vector-valued risk measures using axioms and shows they ignore dependence structures of input random vectors, unlike set-valued risk measures. Convex vector-valued risk measures are
The paper empirically analyzes the ECB's climate-risk-related supervisory efforts' impact on banks' climate risk exposure, management, and green finance activities. Using a difference-in-difference se