Does the asymmetric exponential power distribution improve systemic risk measurement?

Shu Wu, Huiqiong Chen and Helong Li

Journal of Risk Model Validation

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.

References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.risk.net/journal-of-risk-model-validat ... mic-risk-measurement (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:rsk:journ5:7956068

Access Statistics for this article

More articles in Journal of Risk Model Validation from Journal of Risk Model Validation
Bibliographic data for series maintained by Thomas Paine ().