Quantile correlation coefficient: a new tail dependence measure

TL;DR

This paper introduces the quantile correlation coefficient, a new tail dependence measure based on quantile regression slopes, enabling better assessment of tail dependence and asymmetry in data.

Contribution

It proposes a novel tail dependence measure called quantile correlation, along with statistical tests and properties, supported by theoretical proofs and empirical studies.

Findings

Quantile correlation effectively captures tail dependence.

The method provides tests for tail dependence and asymmetry.

Finite sample performance is validated through Monte Carlo simulations.

Abstract

We propose a new measure related with tail dependence in terms of correlation: quantile correlation coefficient of random variables X, Y. The quantile correlation is defined by the geometric mean of two quantile regression slopes of X on Y and Y on X in the same way that the Pearson correlation is related with the regression coefficients of Y on X and X on Y. The degree of tail dependent association in X, Y, if any, is well reflected in the quantile correlation. The quantile correlation makes it possible to measure sensitivity of a conditional quantile of a random variable with respect to change of the other variable. The properties of the quantile correlation are similar to those of the correlation. This enables us to interpret it from the perspective of correlation, on which tail dependence is reflected. We construct measures for tail dependent correlation and tail asymmetry and develop statistical tests for them. We prove asymptotic normality of the estimated quantile correlation and limiting null distributions of the proposed tests, which is well supported in finite samples by a Monte-Carlo study. The proposed quantile correlation methods are well illustrated by analyzing birth weight data set and stock return data set.