Dependent Conditional Value-at-Risk for Aggregate Risk Models

TL;DR

This paper introduces Dependent CoVaR (DCoVaR), a new coherent risk measure for dependent losses, demonstrating its superior performance over existing measures through simulations and empirical financial data analysis.

Contribution

The paper proposes DCoVaR, a novel dependent risk measure that extends CoVaR to account for dependencies, outperforming existing measures like MCoVaR and CCoVaR.

Findings

DCoVaR outperforms MCoVaR and CCoVaR in simulations.

Numerical simulations illustrate the effectiveness of DCoVaR.

Empirical analysis on financial data validates DCoVaR's practical utility.

Abstract

Risk measure forecast and model have been developed in order to not only provide better forecast but also preserve its (empirical) property especially coherent property. Whilst the widely used risk measure of Value-at-Risk (VaR) has shown its performance and benefit in many applications, it is in fact not a coherent risk measure. Conditional VaR (CoVaR), defined as mean of losses beyond VaR, is one of alternative risk measures that satisfies coherent property. There has been several extensions of CoVaR such as Modified CoVaR (MCoVaR) and Copula CoVaR (CCoVaR). In this paper, we propose another risk measure, called Dependent CoVaR (DCoVaR), for a target loss that depends on another random loss, including model parameter treated as random loss. It is found that our DCoVaR outperforms than both MCoVaR and CCoVaR. Numerical simulation is carried out to illustrate the proposed DCoVaR. In addition, we do an empirical study of financial returns data to compute the DCoVaR forecast for heteroscedastic process.