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.
翻译:自然和经济灾害等极端事件对社会产生持久的影响,并促使从数据中分析极端因素。虽然基于高山分布的古典统计工具注重平均行为,在估计极端情况时可能导致持续的偏差,但极端价值理论(EVT)为准确描述极端情况提供了数学基础。在本文中,我们调整了最近采用的动态极端价值模型,从高频数据预测金融风险到自然灾害预测。我们通过对Piton de la Fournaise火山的个案研究,显示了其广泛适用性和灵活性。通过预测性表现,显示了使用EVT知情阈值确定和模拟极端事件的价值。