A wee exploration of techniques for risk assessments of extreme events

Assessing the behaviour of extreme events in univariate and multivariate settings entails many challenges, from the need to capture different sources of non-stationarity to adequately extrapolate into the tail of the distribution and compute probabilities of extreme events associated with high-dimensional vectors. Motivated by these common issues, we use a combination of methods from extreme-value theory, dimensionality reduction, non-parametric statistics, copula theory, and bootstrap model averaging to provide estimates of risk measures associated with environmental extremes. The work is tailored to the four data challenges presented in the EVA (2023) Conference Data Challenge competition, and the methods introduced here represent the approach taken by the Wee Extremes group.