Tail maximal dependence in bivariate models: estimation and applications

TL;DR

This paper introduces a statistical method to accurately estimate the maximum possible dependence in bivariate models, addressing limitations of traditional tail dependence indices in financial risk management.

Contribution

It proposes a novel estimation procedure for maximal tail dependence, improving the assessment of extreme co-movements in financial data.

Findings

The new estimator captures stronger dependence than traditional indices.

Simulation studies demonstrate improved accuracy of the method.

Application to real financial data confirms practical usefulness.

Abstract

Assessing dependence within co-movements of financial instruments has been of much interest in risk management. Typically, indices of tail dependence are used to quantify the strength of such dependence, although many of the indices underestimate the strength. Hence, we advocate the use of a statistical procedure designed to estimate the maximal strength of dependence that can possibly occur among the co-movements. We illustrate the procedure using simulated and real data-sets.