2022/06/06 by Leeuwenkamp, Aleksy
#FOS: Economics and business #General Economics (econ.GN) #General Finance (q-fin.GN) #Risk Management (q-fin.RM)
paper · doi:10.48550/arxiv.2206.02582
This paper shows that the CoVaR,Δ-CoVaR,CoES,Δ-CoES and MES systemic risk measures can be represented in terms of the univariate risk measure evaluated at a quantile determined by the copula. The result is applied to derive empirically relevant properties of these measures concerning their sensitivity to power-law tails, outliers and their properties under aggregation. Furthermore, a novel empirical estimator for the CoES is proposed. The power-law result is applied to derive a novel empirical estimator for the power-law coefficient which depends on Δ-CoVaR/Δ-CoES. To show empirical performance simulations and an application of the methods to a large dataset of financial institutions are used. This paper finds that the MES is not suitable for measuring extreme risks. Also, the ES-based measures are more sensitive to power-law tails and large losses. This makes these measures more useful for measuring network risk but less so for systemic risk. The robustness analysis also shows that all Δ measures can underestimate due to the occurrence of intermediate losses. Lastly, it is found that the power-law tail coefficient estimator can be used as an early-warning indicator of systemic risk.