Journal of Risk Model Validation

Does the asymmetric exponential power distribution improve systemic risk measurement?

Shu Wu, Huiqiong Chen and Helong Li

  • 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.

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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