Assessing bivariate tail non-exchangeable dependence

Autor: Lei Hua, Paramahansa Pramanik, Alan M. Polansky
Rok vydání: 2019
Předmět:
Zdroj: Statistics & Probability Letters. 155:108556
ISSN: 0167-7152
Popis: Non-exchangeable dependence structures exist in the real world. In particular, when dependence patterns in joint distributional tails are important, such as in the fields of engineering, environmetrics and econometrics, one may need to detect the existence, and assess the strength of non-exchangeable dependence patterns in the tails. In this paper, we propose a sensible metric to quantify the degree of tail non-exchangeability of bivariate copulas. Based on the metric, we propose a practical method of assessing tail non-exchangeable dependence with uniform scores of bivariate data. An empirical example is used to demonstrate the usefulness of the proposed method.
Databáze: OpenAIRE