Abstract
We propose an extension of CoVaR approach by employing the Asymmetric Exponential Power Distribution (AEPD) to capture the properties of financial data series such as fat-tailedness and skewness. We prove the new model with AEPD has better goodness-of-fit than traditional model with Gaussian distribution, which means a higher precision. Basing on the Chinese stock market data and the new model, we measure the contribution of 29 financial institutions in bank, security, insurance and other industries.
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Appendix
The time trend of ΔCoVaR
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Li, H., Luo, T., Li, L., Liu, T. (2018). Measuring Systemic Risk in the Chinese Financial System Based on Asymmetric Exponential Power Distribution. In: Tavana, M., Patnaik, S. (eds) Recent Developments in Data Science and Business Analytics. Springer Proceedings in Business and Economics. Springer, Cham. https://doi.org/10.1007/978-3-319-72745-5_24
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DOI: https://doi.org/10.1007/978-3-319-72745-5_24
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