  Extreme events such as natural and economic disasters leave lasting impacts
on society and motivate the analysis of extremes from data. While classical
statistical tools based on Gaussian distributions focus on average behaviour
and can lead to persistent biases when estimating extremes, extreme value
theory (EVT) provides the mathematical foundations to accurately characterise
extremes. In this paper, we adapt a dynamic extreme value model recently
introduced to forecast financial risk from high frequency data to the context
of natural hazard forecasting. We demonstrate its wide applicability and
flexibility using a case study of the Piton de la Fournaise volcano. The value
of using EVT-informed thresholds to identify and model extreme events is shown
through forecast performance.
