A0355
Title: Directional dependence of extreme events
Authors: Maxime Nicolas - University College London (United Kingdom) [presenting]
Matthieu Garcin - Leonard de Vinci Pole Universitaire (France)
Abstract: A novel measure is introduced to quantify the directional dependence of extreme events. The focus is on capturing asymmetric tail dependence by studying the conditional tail expectation of rank-transformed variables, thereby quantifying the behavior of one variable when the other is extreme. The effectiveness of the approach is demonstrated through an extensive simulation framework, and the theoretical asymptotic behavior of the estimator is explored. It is discussed how this measure can help identify causality in extreme events. Finally, the approach is applied to financial and environmental datasets, such as wind speed and temperature extremes, as well as stock-bond market extremes. The results highlight the strong asymmetric nature of extreme events.
