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A stable systemic risk ranking in China’s banking sector: Based on principal component analysis

Author

Listed:
  • Fang, Libing
  • Xiao, Binqing
  • Yu, Honghai
  • You, Qixing

Abstract

In this paper, we compare five popular systemic risk rankings, and apply principal component analysis (PCA) model to provide a stable systemic risk ranking for the Chinese banking sector. Our empirical results indicate that five methods suggest vastly different systemic risk rankings for the same bank, while the combined systemic risk measure based on PCA provides a reliable ranking. Furthermore, according to factor loadings of the first component, PCA combined ranking is mainly based on fundamentals instead of market price data. We clearly find that price-based rankings are not as practical a method as fundamentals-based ones. This PCA combined ranking directly shows systemic risk contributions of each bank for banking supervision purpose and reminds banks to prevent and cope with the financial crisis in advance.

Suggested Citation

  • Fang, Libing & Xiao, Binqing & Yu, Honghai & You, Qixing, 2018. "A stable systemic risk ranking in China’s banking sector: Based on principal component analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 492(C), pages 1997-2009.
    • Handle: RePEc:eee:phsmap:v:492:y:2018:i:c:p:1997-2009
    DOI: 10.1016/j.physa.2017.11.115
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    Citations

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    Cited by:

    1. Nivorozhkin, Eugene & Chondrogiannis, Ilias, 2022. "Shifting balances of systemic risk in the Chinese banking sector: Determinants and trends," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 76(C).
    2. Wang, Yan & Wang, Yue & Li, Ming-Xia, 2019. "Regional characteristics of sports industry profitability: Evidence from China’s province level data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 946-955.
    3. Zhang, Xingmin & Fu, Qiang & Lu, Liping & Wang, Qingyu & Zhang, Shuai, 2021. "Bank liquidity creation, network contagion and systemic risk: Evidence from Chinese listed banks," Journal of Financial Stability, Elsevier, vol. 53(C).
    4. Jie Sun & Mengjie Zhou & Wenguo Ai & Hui Li, 2019. "Dynamic prediction of relative financial distress based on imbalanced data stream: from the view of one industry," Risk Management, Palgrave Macmillan, vol. 21(4), pages 215-242, December.
    5. Cevik, Emrah Ismail & Caliskan Terzioglu, Hande & Kilic, Yunus & Bugan, Mehmet Fatih & Dibooglu, Sel, 2024. "Interconnectedness and systemic risk: Evidence from global stock markets," Research in International Business and Finance, Elsevier, vol. 69(C).
    6. Xu, Qifa & Chen, Lu & Jiang, Cuixia & Yuan, Jing, 2018. "Measuring systemic risk of the banking industry in China: A DCC-MIDAS-t approach," Pacific-Basin Finance Journal, Elsevier, vol. 51(C), pages 13-31.
    7. Matteo Foglia & Eliana Angelini, 2021. "The triple (T3) dimension of systemic risk: Identifying systemically important banks," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(1), pages 7-26, January.
    8. Chengzhao, Zhang & Heping, Pan & Yu, Ma & Xun, Huang, 2019. "Analysis of Asia Pacific stock markets with a novel multiscale model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).

    More about this item

    Keywords

    Systemic risk rankings; Principal component analysis; Fundamentals; Banking supervision;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications

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