Modelling dependence of extreme events in energy markets using tail copulas

Abstract

This paper studies, for the first time, the dependence of extreme events in energy markets. Based on a large data set comprising quotes of crude oil and natural gas futures, we estimate and model large co-movements of commodity returns. To detect the presence of tail dependence we apply a new method based on the concept of tail copulas which accounts for different scenarios of joint extreme outcomes. Moreover, we show that the common practice to fit copulas to the data cannot capture the dynamics in the tail of the joint distribution and, therefore, is unsuitable for risk management purposes.