Advertisement

Measuring Systemic Risk in the Chinese Financial System Based on Asymmetric Exponential Power Distribution

  • Helong Li
  • Tianqi Luo
  • Liuling Li
  • Tiancheng Liu
Conference paper
Part of the Springer Proceedings in Business and Economics book series (SPBE)

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.

Keywords

Asymmetric Exponential Power Distribution (AEPD) Systemic Risk Conditional Value-at-Risk (CoVaR) 

References

  1. 1.
    Giulio, G., & Tolga Ergün, A. (2013). Systemic risk measurement: Multivariate GARCH estimation of CoVaR. Journal of Banking & Finance, 37, 3169–3180.CrossRefGoogle Scholar
  2. 2.
    López-Espinosa, G., Moreno, A., Rubia, A., & Valderrama, L. (2015). Systemic risk and asymmetric responses in the financial industry. Journal of Banking & Finance, 58, 471–485.CrossRefGoogle Scholar
  3. 3.
    Slijkerman, F. J., Schoenmaker, D., & de Vries, C. G. (2013). Systemic risk and diversification across European banks and insurers. Journal of Banking & Finance, 37, 773–785.CrossRefGoogle Scholar
  4. 4.
    Straetmans, S., & Chaudhry, S. M. (2015). Tail risk and systemic risk of US and Eurozone financial institutions in the wake of the global financial crisis. Journal of International Money and Finance, 58, 191–223.CrossRefGoogle Scholar
  5. 5.
    Yun, J., & Moonb, H. (2014). Measuring systemic risk in the Korean banking sector via dynamic conditional correlation models. Pacific-Basin Finance Journal, 27, 94–114.CrossRefGoogle Scholar
  6. 6.
    Zhang, R., He, X., & Qi, Y. (2015). Measuring systemic risk of China’s financial system under extreme condition. Statistical Research, 32(9), 30–38.Google Scholar
  7. 7.
    Gao, G., & Pan, Y. (2011). Banking systemic risk based on dynamic CoVaR estimation. Journal of Shanghai Jiaotong University, 45(12), 1753–1759.Google Scholar
  8. 8.
    Liu, X., & Gu, S. (2014). Research on risk spillovers from the real estate department to financial system based on AR-GARCH-CoVaR. Systems Engineering - Theory & Practice, 34(s1), 106–111.Google Scholar
  9. 9.
    Lin, H., Liu, T., & Zhang, P. (2012). An empirical study on systemic risk spillover effects of insurance institutions- based on AR-GARCH-CoVaR model, Beida CCISSR Forum.Google Scholar
  10. 10.
    Zhu, D., & Zinde-Walsh, V. (2009). Properties and estimation of asymmetric exponential power distribution. Journal of Econometrics, 148, 86–99.CrossRefGoogle Scholar
  11. 11.
    Tobias, A., & Brunnermeier, M. K. (2011). CoVaR, Working paper, Federal Reserve Bank of New York.Google Scholar
  12. 12.
    Zhu, D., & Galbraith, J. W. (2011). Modeling and forecasting expected shortfall with the generalized asymmetric Student-t and asymmetric exponential power distributions. Journal of Empirical Finance, 18, 765–778.CrossRefGoogle Scholar
  13. 13.
    Li, L., Gan, Q., Zhuo, Z., & Mizrach, B. (2014). Testing the CAPM theory based on a new model for Fama-French 25 portfolio returns. Theoretical Economics Letters, 04(8), 666–680.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Helong Li
    • 1
  • Tianqi Luo
    • 1
  • Liuling Li
    • 2
  • Tiancheng Liu
    • 3
  1. 1.School of Economics and Commerce, South China University of TechnologyGuangzhouPeople’s Republic of China
  2. 2.Institute of Statistics and Econometrics, Economics School, Nankai UniversityTianjingPeople’s Republic of China
  3. 3.School of Computer, South China University of TechnologyGuangzhouPeople’s Republic of China

Personalised recommendations