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Identifying systemically important financial institutions in China: new evidence from a dynamic copula-CoVaR approach

Author

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  • Fei Wu

    (Southwestern University of Finance and Economics)

  • Zhiwei Zhang

    (Nanjing University of Aeronautics and Astronautics)

  • Dayong Zhang

    (Southwestern University of Finance and Economics)

  • Qiang Ji

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

Abstract

We examine the risk spillovers in the Chinese financial system by adopting a time-varying copula-CoVaR approach. We first identify the systemically important financial institutions for each industry group in China’s financial sector in a dynamic context. We then find strong evidence of upside and downside risk spillovers between these key institutions and the financial system, by quantifying value at risk (VaR), conditional VaR (CoVaR) and delta CoVaR (ΔCoVaR) through time-varying copulas. The empirical results further reveal asymmetric downside and upside risk spillover effects, indicating asymmetric hedging strategies for investors during market upturns and downturns.

Suggested Citation

  • Fei Wu & Zhiwei Zhang & Dayong Zhang & Qiang Ji, 2023. "Identifying systemically important financial institutions in China: new evidence from a dynamic copula-CoVaR approach," Annals of Operations Research, Springer, vol. 330(1), pages 119-153, November.
  • Handle: RePEc:spr:annopr:v:330:y:2023:i:1:d:10.1007_s10479-021-04176-z
    DOI: 10.1007/s10479-021-04176-z
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    as
    1. Betz, Frank & Hautsch, Nikolaus & Peltonen, Tuomas A. & Schienle, Melanie, 2016. "Systemic risk spillovers in the European banking and sovereign network," Journal of Financial Stability, Elsevier, vol. 25(C), pages 206-224.
    2. 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.
    3. Viral V. Acharya & Lasse H. Pedersen & Thomas Philippon & Matthew Richardson, 2017. "Measuring Systemic Risk," The Review of Financial Studies, Society for Financial Studies, vol. 30(1), pages 2-47.
    4. Bernanke, Ben & Gertler, Mark & Gilchrist, Simon, 1996. "The Financial Accelerator and the Flight to Quality," The Review of Economics and Statistics, MIT Press, vol. 78(1), pages 1-15, February.
    5. Banulescu, Georgiana-Denisa & Dumitrescu, Elena-Ivona, 2015. "Which are the SIFIs? A Component Expected Shortfall approach to systemic risk," Journal of Banking & Finance, Elsevier, vol. 50(C), pages 575-588.
    6. Kanno, Masayasu, 2015. "Assessing systemic risk using interbank exposures in the global banking system," Journal of Financial Stability, Elsevier, vol. 20(C), pages 105-130.
    7. 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.
    8. Ji, Qiang & Liu, Bing-Yue & Fan, Ying, 2019. "Risk dependence of CoVaR and structural change between oil prices and exchange rates: A time-varying copula model," Energy Economics, Elsevier, vol. 77(C), pages 80-92.
    9. Bernal, Oscar & Gnabo, Jean-Yves & Guilmin, Grégory, 2014. "Assessing the contribution of banks, insurance and other financial services to systemic risk," Journal of Banking & Finance, Elsevier, vol. 47(C), pages 270-287.
    10. Philippas, Dionisis & Siriopoulos, Costas, 2013. "Putting the “C” into crisis: Contagion, correlations and copulas on EMU bond markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 27(C), pages 161-176.
    11. 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.
    12. Liu, Bing-Yue & Ji, Qiang & Fan, Ying, 2017. "Dynamic return-volatility dependence and risk measure of CoVaR in the oil market: A time-varying mixed copula model," Energy Economics, Elsevier, vol. 68(C), pages 53-65.
    13. 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.
    14. Mensi, Walid & Hammoudeh, Shawkat & Shahzad, Syed Jawad Hussain & Shahbaz, Muhammad, 2017. "Modeling systemic risk and dependence structure between oil and stock markets using a variational mode decomposition-based copula method," Journal of Banking & Finance, Elsevier, vol. 75(C), pages 258-279.
    15. Fang, Libing & Sun, Boyang & Li, Huijing & Yu, Honghai, 2018. "Systemic risk network of Chinese financial institutions," Emerging Markets Review, Elsevier, vol. 35(C), pages 190-206.
    16. Yao, Shujie & He, Hongbo & Chen, Shou & Ou, Jinghua, 2018. "Financial liberalization and cross-border market integration: Evidence from China's stock market," International Review of Economics & Finance, Elsevier, vol. 58(C), pages 220-245.
    17. 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).
    18. Reboredo, Juan C. & Ugolini, Andrea, 2015. "Systemic risk in European sovereign debt markets: A CoVaR-copula approach," Journal of International Money and Finance, Elsevier, vol. 51(C), pages 214-244.
    19. Jin, Xiaoye, 2018. "Downside and upside risk spillovers from China to Asian stock markets: A CoVaR-copula approach," Finance Research Letters, Elsevier, vol. 25(C), pages 202-212.
    20. Hans Manner & Olga Reznikova, 2012. "A Survey on Time-Varying Copulas: Specification, Simulations, and Application," Econometric Reviews, Taylor & Francis Journals, vol. 31(6), pages 654-687, November.
    21. Christian Brownlees & Robert F. Engle, 2017. "SRISK: A Conditional Capital Shortfall Measure of Systemic Risk," The Review of Financial Studies, Society for Financial Studies, vol. 30(1), pages 48-79.
    22. Härdle, Wolfgang Karl & Wang, Weining & Yu, Lining, 2016. "TENET: Tail-Event driven NETwork risk," Journal of Econometrics, Elsevier, vol. 192(2), pages 499-513.
    23. Wu, Fei, 2019. "Sectoral contributions to systemic risk in the Chinese stock market," Finance Research Letters, Elsevier, vol. 31(C).
    24. Pragidis, I.C. & Aielli, G.P. & Chionis, D. & Schizas, P., 2015. "Contagion effects during financial crisis: Evidence from the Greek sovereign bonds market," Journal of Financial Stability, Elsevier, vol. 18(C), pages 127-138.
    25. Sedunov, John, 2016. "What is the systemic risk exposure of financial institutions?," Journal of Financial Stability, Elsevier, vol. 24(C), pages 71-87.
    26. Glasserman, Paul & Young, H. Peyton, 2015. "How likely is contagion in financial networks?," Journal of Banking & Finance, Elsevier, vol. 50(C), pages 383-399.
    27. Alexandra Dias & Paul Embrechts, 2009. "Testing for structural changes in exchange rates' dependence beyond linear correlation," The European Journal of Finance, Taylor & Francis Journals, vol. 15(7-8), pages 619-637.
    28. Andrew J. Patton, 2006. "Modelling Asymmetric Exchange Rate Dependence," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 47(2), pages 527-556, May.
    29. Viral Acharya & Robert Engle & Matthew Richardson, 2012. "Capital Shortfall: A New Approach to Ranking and Regulating Systemic Risks," American Economic Review, American Economic Association, vol. 102(3), pages 59-64, May.
    30. Hansen, Bruce E, 1994. "Autoregressive Conditional Density Estimation," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 35(3), pages 705-730, August.
    31. Patton, Andrew J., 2012. "A review of copula models for economic time series," Journal of Multivariate Analysis, Elsevier, vol. 110(C), pages 4-18.
    32. Bernardi, M. & Durante, F. & Jaworski, P., 2017. "CoVaR of families of copulas," Statistics & Probability Letters, Elsevier, vol. 120(C), pages 8-17.
    33. Ji, Qiang & Bouri, Elie & Roubaud, David & Shahzad, Syed Jawad Hussain, 2018. "Risk spillover between energy and agricultural commodity markets: A dependence-switching CoVaR-copula model," Energy Economics, Elsevier, vol. 75(C), pages 14-27.
    34. Zhang, Dayong, 2017. "Oil shocks and stock markets revisited: Measuring connectedness from a global perspective," Energy Economics, Elsevier, vol. 62(C), pages 323-333.
    35. Abadie A., 2002. "Bootstrap Tests for Distributional Treatment Effects in Instrumental Variable Models," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 284-292, March.
    36. Reboredo, Juan C. & Rivera-Castro, Miguel A. & Ugolini, Andrea, 2016. "Downside and upside risk spillovers between exchange rates and stock prices," Journal of Banking & Finance, Elsevier, vol. 62(C), pages 76-96.
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