Copula-based measures of asymmetry between the lower and upper tail probabilities

TL;DR

This paper introduces a copula-based measure to quantify asymmetry between lower and upper tail probabilities in bivariate distributions, with theoretical properties, estimation methods, and applications to financial data.

Contribution

It proposes a new, simple copula-based asymmetry measure, analyzes its properties, and develops estimation and testing procedures with real-world financial applications.

Findings

The measure has desirable properties and simple limits.

Sample analogues converge to Gaussian processes.

Application to financial data demonstrates practical utility.

Abstract

We propose a copula-based measure of asymmetry between the lower and upper tail probabilities of bivariate distributions. The proposed measure has a simple form and possesses some desirable properties as a measure of asymmetry. The limit of the proposed measure as the index goes to the boundary of its domain can be expressed in a simple form under certain conditions on copulas. A sample analogue of the proposed measure for a sample from a copula is presented and its weak convergence to a Gaussian process is shown. Another sample analogue of the presented measure, which is based on a sample from a distribution on $\mathbb{R}^2$, is given. Simple methods for interval estimation and nonparametric testing based on the two sample analogues are presented. As an example, the presented measure is applied to daily returns of S&P500 and Nikkei225.