IDEAS home Printed from https://ideas.repec.org/a/vls/finstu/v25y2021i3p6-28.html
   My bibliography  Save this article

Measuring Systemic Risk Of China'S Listed Banks

Author

Listed:
  • ZHANG, Ping

    (Department of Financial Engineering, School of Finance, Capital University of Economics and Business, Beijing, China)

  • WANG, Yiru

    (School of Finance, Capital University of Economics and Business, Beijing, China)

  • ZHAO, Min

    (Department of Financial Engineering, School of Finance, Capital University of Economics and Business, Beijing, China)

  • YANG, Tzu-Yi

    (Department of Foreign Languages and Literature National Ilan University, Taiwan)

Abstract

After the financial crisis in 2008, the world became more aware of the importance of the systemic risk. Within China’s financial system, commercial banks have a dominant position. Therefore, the study of systemic risk of the banking industry in China has an important and real meaning. The present paper was based on the weekly return of 16 listed banks in China from 2010 to 2018. The quantile regression method and the GARCH model were applied to measure the systemic risk of banks in China. The VaR and CoVaR showed that the risk of large commercial banks in China was generally low but was usually higher than the medium and small banks. Comparing the quantile regression method and the GARCH model method indicated that both approaches could effectively measure the systemic risk of listed banks in China. The %CoVAR calculated by the GARCH model was significantly smaller than the result from the quantile regression method. Compared with the DCC-GARCH model, a simple GARCH model might underestimate the systemic risk of banks.

Suggested Citation

  • ZHANG, Ping & WANG, Yiru & ZHAO, Min & YANG, Tzu-Yi, 2021. "Measuring Systemic Risk Of China'S Listed Banks," Studii Financiare (Financial Studies), Centre of Financial and Monetary Research "Victor Slavescu", vol. 25(3), pages 6-28, September.
  • Handle: RePEc:vls:finstu:v:25:y:2021:i:3:p:6-28
    as

    Download full text from publisher

    File URL: http://www.icfm.ro/RePEc/vls/vls_pdf/vol25i3p6-28.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Diebold, Francis X. & Yılmaz, Kamil, 2014. "On the network topology of variance decompositions: Measuring the connectedness of financial firms," Journal of Econometrics, Elsevier, vol. 182(1), pages 119-134.
    2. Viral V. Acharya & Lasse H. Pedersen & Thomas Philippon & Matthew Richardson, 2017. "Measuring Systemic Risk," Review of Financial Studies, Society for Financial Studies, vol. 30(1), pages 2-47.
    3. Billio, Monica & Getmansky, Mila & Lo, Andrew W. & Pelizzon, Loriana, 2012. "Econometric measures of connectedness and systemic risk in the finance and insurance sectors," Journal of Financial Economics, Elsevier, vol. 104(3), pages 535-559.
    4. Lundgren, Amanda Ivarsson & Milicevic, Adriana & Uddin, Gazi Salah & Kang, Sang Hoon, 2018. "Connectedness network and dependence structure mechanism in green investments," Energy Economics, Elsevier, vol. 72(C), pages 145-153.
    5. Graciela Kaminsky & Saul Lizondo & Carmen M. Reinhart, 1998. "Leading Indicators of Currency Crises," IMF Staff Papers, Palgrave Macmillan, vol. 45(1), pages 1-48, March.
    6. Christian Brownlees & Robert F. Engle, 2017. "SRISK: A Conditional Capital Shortfall Measure of Systemic Risk," Review of Financial Studies, Society for Financial Studies, vol. 30(1), pages 48-79.
    7. Brunetti, Celso & Harris, Jeffrey H. & Mankad, Shawn & Michailidis, George, 2019. "Interconnectedness in the interbank market," Journal of Financial Economics, Elsevier, vol. 133(2), pages 520-538.
    8. Yan Wang & Shoudong Chen & Xiu Zhang, 2014. "Measuring systemic financial risk and analyzing influential factors: an extreme value approach," China Finance Review International, Emerald Group Publishing Limited, vol. 4(4), pages 385-398, November.
    9. Corsi, Fulvio & Lillo, Fabrizio & Pirino, Davide & Trapin, Luca, 2018. "Measuring the propagation of financial distress with Granger-causality tail risk networks," Journal of Financial Stability, Elsevier, vol. 38(C), pages 18-36.
    10. Nishimura, Yusaku & Sun, Bianxia, 2018. "The intraday volatility spillover index approach and an application in the Brexit vote," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 55(C), pages 241-253.
    11. Berisha, Edmond & Meszaros, John & Olson, Eric, 2018. "Income inequality, equities, household debt, and interest rates: Evidence from a century of data," Journal of International Money and Finance, Elsevier, vol. 80(C), pages 1-14.
    12. Diebold, Francis X. & Yilmaz, Kamil, 2012. "Better to give than to receive: Predictive directional measurement of volatility spillovers," International Journal of Forecasting, Elsevier, vol. 28(1), pages 57-66.
    13. Ghulam, Yaseen & Doering, Jana, 2018. "Spillover effects among financial institutions within Germany and the United Kingdom," Research in International Business and Finance, Elsevier, vol. 44(C), pages 49-63.
    14. Zaichao Du & Juan Carlos Escanciano, 2017. "Backtesting Expected Shortfall: Accounting for Tail Risk," Management Science, INFORMS, vol. 63(4), pages 940-958, April.
    15. Maghyereh, Aktham I. & Awartani, Basel & Bouri, Elie, 2016. "The directional volatility connectedness between crude oil and equity markets: New evidence from implied volatility indexes," Energy Economics, Elsevier, vol. 57(C), pages 78-93.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Li, Yanshuang & Zhuang, Xintian & Wang, Jian, 2021. "Analysis of the cross-region risk contagion effect in stock market based on volatility spillover networks: Evidence from China," The North American Journal of Economics and Finance, Elsevier, vol. 56(C).
    2. Foglia, Matteo & Addi, Abdelhamid & Angelini, Eliana, 2022. "The Eurozone banking sector in the time of COVID-19: Measuring volatility connectedness," Global Finance Journal, Elsevier, vol. 51(C).
    3. Wang, Gang-Jin & Xie, Chi & Zhao, Longfeng & Jiang, Zhi-Qiang, 2018. "Volatility connectedness in the Chinese banking system: Do state-owned commercial banks contribute more?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 57(C), pages 205-230.
    4. Foglia, Matteo & Addi, Abdelhamid & Wang, Gang-Jin & Angelini, Eliana, 2022. "Bearish Vs Bullish risk network: A Eurozone financial system analysis," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 77(C).
    5. Li, Yanshuang & Zhuang, Xintian & Wang, Jian & Zhang, Weiping, 2020. "Analysis of the impact of Sino-US trade friction on China’s stock market based on complex networks," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
    6. Shi, Huai-Long & Zhou, Wei-Xing, 2022. "Factor volatility spillover and its implications on factor premia," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 80(C).
    7. Shi Chen & Wolfgang Karl Hardle & Brenda L'opez Cabrera, 2020. "Regularization Approach for Network Modeling of German Power Derivative Market," Papers 2009.09739, arXiv.org.
    8. Xiaoyu Liu & Xiaoli Chen, 2021. "Can “Concerted” Macroprudential Policies Mitigate Cross‐border Contagion of Financial Risks? Evidence from China and Its Financially Connected Economies," China & World Economy, Institute of World Economics and Politics, Chinese Academy of Social Sciences, vol. 29(3), pages 26-54, May.
    9. Wang, Gang-Jin & Chen, Yang-Yang & Si, Hui-Bin & Xie, Chi & Chevallier, Julien, 2021. "Multilayer information spillover networks analysis of China’s financial institutions based on variance decompositions," International Review of Economics & Finance, Elsevier, vol. 73(C), pages 325-347.
    10. Xue Cui & Lu Yang, 2024. "Systemic risk and idiosyncratic networks among global systemically important banks," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 29(1), pages 58-75, January.
    11. Jean-Baptiste Hasse, 2022. "Systemic risk: a network approach," Empirical Economics, Springer, vol. 63(1), pages 313-344, July.
    12. Andrieş, Alin Marius & Ongena, Steven & Sprincean, Nicu & Tunaru, Radu, 2022. "Risk spillovers and interconnectedness between systemically important institutions," Journal of Financial Stability, Elsevier, vol. 58(C).
    13. Liu, Bing-Yue & Fan, Ying & Ji, Qiang & Hussain, Nazim, 2022. "High-dimensional CoVaR network connectedness for measuring conditional financial contagion and risk spillovers from oil markets to the G20 stock system," Energy Economics, Elsevier, vol. 105(C).
    14. Feng, Yusen & Wang, Gang-Jin & Zhu, You & Xie, Chi, 2023. "Systemic risk spillovers and the determinants in the stock markets of the Belt and Road countries," Emerging Markets Review, Elsevier, vol. 55(C).
    15. Wu, Fei & Zhang, Dayong & Zhang, Zhiwei, 2019. "Connectedness and risk spillovers in China’s stock market: A sectoral analysis," Economic Systems, Elsevier, vol. 43(3).
    16. Singh, Vipul Kumar & Nishant, Shreyank & Kumar, Pawan, 2018. "Dynamic and directional network connectedness of crude oil and currencies: Evidence from implied volatility," Energy Economics, Elsevier, vol. 76(C), pages 48-63.
    17. Gang-Jin Wang & Chi Xie & Kaijian He & H. Eugene Stanley, 2017. "Extreme risk spillover network: application to financial institutions," Quantitative Finance, Taylor & Francis Journals, vol. 17(9), pages 1417-1433, September.
    18. Ehsan Bagheri & Seyed Babak Ebrahimi & Arman Mohammadi & Mahsa Miri & Stelios Bekiros, 2022. "The Dynamic Volatility Connectedness Structure of Energy Futures and Global Financial Markets: Evidence From a Novel Time–Frequency Domain Approach," Computational Economics, Springer;Society for Computational Economics, vol. 59(3), pages 1087-1111, March.
    19. Zhang, Ping & Yin, Shiqi & Sha, Yezhou, 2023. "Global systemic risk dynamic network connectedness during the COVID-19: Evidence from nonlinear Granger causality," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 85(C).
    20. Xiaoyong Xiao & Jing Huang, 2018. "Dynamic Connectedness of International Crude Oil Prices: The Diebold–Yilmaz Approach," Sustainability, MDPI, vol. 10(9), pages 1-16, September.

    More about this item

    Keywords

    systemic risk; CoVaR; quantile regression method; GARCH model method; DCC-GARCH Pages: 6-28;
    All these keywords.

    JEL classification:

    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
    • G00 - Financial Economics - - General - - - General

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:vls:finstu:v:25:y:2021:i:3:p:6-28. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Daniel Mateescu (email available below). General contact details of provider: https://edirc.repec.org/data/cfiarro.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.