B1602
Title: Exploring tail dependence between time series via concomitants
Authors: Amir Khorrami Chokami - University of Turin (Italy) [presenting]
Marie Kratz - ESSEC Business School, CREAR (France)
Michel Dacorogna - Prime Re Solutions (Switzerland)
Abstract: The problem of finding methods to describe the extremal dependence among multiple time series has rapidly become attractive in recent years due to the vast variety of fields where its practical implications are of interest. However, providing handy tools to assess such dependence is still challenging. A prior study has proposed an empirical method to explore the tail dependence between mortality and financial market risks. This study is revisited, focusing on data as a larger dataset containing extreme risks, such as the recent pandemic, is explored. It is also a way to go further in the analytical development of the method, formalized with concomitants and using a past study, then testing the theoretical results on data.
