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Interest rate risk of Chinese commercial banks based on the GARCH-EVT model

Author

Listed:
  • Xin Chen

    (Sanjiang University)

  • Zhangming Shan

    (Nanjing University of Information Science & Technology)

  • Decai Tang

    (Sanjiang University)

  • Biao Zhou

    (Nanjing University of Finance and Economics)

  • Valentina Boamah

    (Nanjing University of Information Science & Technology)

Abstract

Interest rate market risk faced by China’s commercial banks is increasing after the announcement that the interest rate marketisation is completed. This paper examines the Value-at-Risk, and statistical properties in the daily price return of Shanghai banks’ overnight offered rate. The study applies two-stage approaches, combining GARCH-type models with extreme value theory. Firstly, the Markov regime switching model is used to test the regime states of the series. Secondly, the performance of different VaR models are examined. Results show that the extreme value approach estimates better at the 99% confidence level. The EGARCH-GED model is the most suitable of the employed GARCH-type models. The back-testing results support the idea that the approach used in this study is appropriate for improving commercial banks’ daily risk management. This paper applies the GARCH-EVT method for interest rate measurement after China’s interest rate marketisation and added regime analysis of interest rate. Suggested policy implications will help formulate policies that guide the activities of commercial banks in China.

Suggested Citation

  • Xin Chen & Zhangming Shan & Decai Tang & Biao Zhou & Valentina Boamah, 2023. "Interest rate risk of Chinese commercial banks based on the GARCH-EVT model," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-11, December.
    • Handle: RePEc:pal:palcom:v:10:y:2023:i:1:d:10.1057_s41599-023-02321-6
    DOI: 10.1057/s41599-023-02321-6
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    2. Müzeyyen Çiğdem Akbaş, 2024. "Measuring the impact of enterprise risk management on performance, value, and risk indicators of Borsa Istanbul XBANK companies with data mining prediction models," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-19, December.

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