Does the asymmetric exponential power distribution improve systemic risk measurement?

Shu Wu; Huiqiong Chen; Helong Li

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The authors compare the gooodness-of-fit of AEPD with normal, Student and skewed t distribution in Chinese banking sector, finding that AEPD has the best fit.
The result shows that unit risk of large commercial banks has a stronger impact to the banking system, large commercial banks have better ability of risk management and lower individual risk and that in non-turbulent periods, large commercial banks spillover almost same size of risk as joint-stock and municipal banks to the system, while in turbulent events such as that in 2015, the large commercial banks leapt as the most dangerous risk contagion source.
The estimated decay rates of density in the left and right tails show that there is a stronger fat-tailedness in the left tail than the right tail, giving the evidence of extreme instability in a negative market.

Abstract

The measurement of systemic risk using parametric modeling suffers from fat-tailedness, asymmetric kurtosis and asymmetric tails. Prior research shows that the asymmetric exponential power distribution (AEPD) can potentially avoid overfitting and underfitting problems because it can be reduced to a Gaussian distribution and a generalized error distribution. This paper implements a parametric estimation for the systemic risk measure CoVaR (ie, conditional value-at-risk) of Huang and Uryasev and compares the goodness-of-fit and backtesting performance of the AEPD with other commonly used distributions (ie, the normal, Student t and skewed t distributions). Based on data from the Chinese banking sector from 2008 to 2019, the empirical results show that AEPD has the best goodness-of-fit. Moreover, it is the only distribution that provides a validated estimation for CoVaR.

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