Involving copula functions in Conditional Tail Expectation

TL;DR

This paper introduces a new risk measure called Copula Conditional Tail Expectation that accounts for loss fluctuations and correlations between variables, enhancing tail risk assessment with real financial data application.

Contribution

It proposes a novel risk measure incorporating copula functions to better evaluate joint tail risks in financial contexts.

Findings

The new measure effectively captures tail dependencies.

Application to financial data demonstrates practical utility.

Provides a more comprehensive risk assessment method.

Abstract

Our goal in this paper is to propose an alternative risk measure which takes into account the fluctuations of losses and possible correlations between random variables. This new notion of risk measures, that we call Copula Conditional Tail Expectation describes the expected amount of risk that can be experienced given that a potential bivariate risk exceeds a bivariate threshold value, and provides an important measure for right-tail risk. An application to real financial data is given.