Abstract

Quantifying tail dependence is an important issue in various fields such as finance and risk management. The prevalent tail dependence coefficient (TDC), however, is known to underestimate the degree of tail dependence and it fails to capture non-exchangeable tail dependence since the TDC evaluates the limiting tail probability only along the main diagonal. To overcome these issues, two novel tail dependence measures called the maximal tail concordance measure (MTCM) and the average tail concordance measure (ATCM) are proposed. Both measures are constructed based on tail copulas and possess clear probabilistic interpretations in that the MTCM evaluates the largest limiting probability among all comparable rectangles in the tail, and the ATCM is a normalized average of these limiting probabilities. In contrast to the TDC, the MTCM and the ATCM can capture non-exchangeable tail dependence. Moreover, they often admit analytical forms, and satisfy axiomatic properties naturally required to quantify tail dependence. Estimators of the two measures are also constructed. A real data analysis reveals striking tail dependence and tail non-exchangeability of the return series of stock indices, particularly in periods of financial distress.