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Measuring systemic risk of the banking industry in China: A DCC-MIDAS-t approach

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  • Xu, Qifa
  • Chen, Lu
  • Jiang, Cuixia
  • Yuan, Jing

Abstract

This paper measures systemic risk in the Chinese banking sector by using the CoVaR approach. First, we introduce the student's t distribution into the standard DCC-MIDAS and propose a DCC-MIDAS-t model, particularly suitable for processing fat-tailed financial returns. We then apply the proposed DCC-MIDAS-t model to measure systemic risk following the idea of CoVaR. The empirical studies on the Chinese banking industry show that the DCC-MIDAS-t model is preferred in terms of the accuracy of volatility prediction and CoVaR measure. Macroeconomic information are proved to be able to improve the accuracy of systemic risk measure and M2 is most useful in comparing with IP and PPI. Several banks, such as BOC and BONB, suffered a lot from the stock market crash in June 2015 and some of them still maintain high risk spillovers during the after-crash period. Our findings suggest that these banks should be the focus of supervision even after the crash.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:pacfin:v:51:y:2018:i:c:p:13-31
    DOI: 10.1016/j.pacfin.2018.05.009
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    as
    1. Friedman, Milton, 1988. "Money and the Stock Market," Journal of Political Economy, University of Chicago Press, vol. 96(2), pages 221-245, April.
    2. Asgharian, Hossein & Christiansen, Charlotte & Hou, Ai Jun, 2015. "Effects of macroeconomic uncertainty on the stock and bond markets," Finance Research Letters, Elsevier, vol. 13(C), pages 10-16.
    3. 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.
    4. 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.
    5. Connor, Gregory & Suurlaht, Anita, 2013. "Dynamic stock market covariances in the Eurozone," Journal of International Money and Finance, Elsevier, vol. 37(C), pages 353-370.
    6. Chami, Ralph & Cosimano, Thomas F., 2010. "Monetary policy with a touch of Basel," Journal of Economics and Business, Elsevier, vol. 62(3), pages 161-175, May.
    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. Modigliani, Franco, 1986. "Life Cycle, Individual Thrift, and the Wealth of Nations," American Economic Review, American Economic Association, vol. 76(3), pages 297-313, June.
    9. John Y. Campbell, Robert J. Shiller, 1988. "The Dividend-Price Ratio and Expectations of Future Dividends and Discount Factors," The Review of Financial Studies, Society for Financial Studies, vol. 1(3), pages 195-228.
    10. Martínez-Jaramillo, Serafín & Pérez, Omar Pérez & Embriz, Fernando Avila & Dey, Fabrizio López Gallo, 2010. "Systemic risk, financial contagion and financial fragility," Journal of Economic Dynamics and Control, Elsevier, vol. 34(11), pages 2358-2374, November.
    11. Robert F. Engle & Eric Ghysels & Bumjean Sohn, 2013. "Stock Market Volatility and Macroeconomic Fundamentals," The Review of Economics and Statistics, MIT Press, vol. 95(3), pages 776-797, July.
    12. Schwert, G William, 1989. " Why Does Stock Market Volatility Change over Time?," Journal of Finance, American Finance Association, vol. 44(5), pages 1115-1153, December.
    13. Yun, Jaeho & Moon, Hyejung, 2014. "Measuring systemic risk in the Korean banking sector via dynamic conditional correlation models," Pacific-Basin Finance Journal, Elsevier, vol. 27(C), pages 94-114.
    14. Guo, Feng & Hu, Jinyan & Jiang, Mingming, 2013. "Monetary shocks and asymmetric effects in an emerging stock market: The case of China," Economic Modelling, Elsevier, vol. 32(C), pages 532-538.
    15. Hossein Asgharian & Charlotte Christiansen & Ai Jun Hou, 2016. "Macro-Finance Determinants of the Long-Run Stock–Bond Correlation: The DCC-MIDAS Specification," Journal of Financial Econometrics, Oxford University Press, vol. 14(3), pages 617-642.
    16. Ghysels, Eric & Santa-Clara, Pedro & Valkanov, Rossen, 2005. "There is a risk-return trade-off after all," Journal of Financial Economics, Elsevier, vol. 76(3), pages 509-548, June.
    17. Hossein Asgharian & Ai Jun Hou & Farrukh Javed, 2013. "The Importance of the Macroeconomic Variables in Forecasting Stock Return Variance: A GARCH‐MIDAS Approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 32(7), pages 600-612, November.
    18. Christian Conrad & Karin Loch, 2015. "Anticipating Long‐Term Stock Market Volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(7), pages 1090-1114, November.
    19. 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.
    20. Colacito, Riccardo & Engle, Robert F. & Ghysels, Eric, 2011. "A component model for dynamic correlations," Journal of Econometrics, Elsevier, vol. 164(1), pages 45-59, September.
    21. Conrad, Christian & Loch, Karin & Rittler, Daniel, 2014. "On the macroeconomic determinants of long-term volatilities and correlations in U.S. stock and crude oil markets," Journal of Empirical Finance, Elsevier, vol. 29(C), pages 26-40.
    22. Rösch, Daniel & Scheule, Harald, 2016. "The role of loan portfolio losses and bank capital for Asian financial system resilience," Pacific-Basin Finance Journal, Elsevier, vol. 40(PB), pages 289-305.
    23. Paul H. Kupiec, 1995. "Techniques for verifying the accuracy of risk measurement models," Finance and Economics Discussion Series 95-24, Board of Governors of the Federal Reserve System (U.S.).
    24. Girardin, Eric & Joyeux, Roselyne, 2013. "Macro fundamentals as a source of stock market volatility in China: A GARCH-MIDAS approach," Economic Modelling, Elsevier, vol. 34(C), pages 59-68.
    25. Girardi, Giulio & Tolga Ergün, A., 2013. "Systemic risk measurement: Multivariate GARCH estimation of CoVaR," Journal of Banking & Finance, Elsevier, vol. 37(8), pages 3169-3180.
    26. Ghysels, Eric & Santa-Clara, Pedro & Valkanov, Rossen, 2006. "Predicting volatility: getting the most out of return data sampled at different frequencies," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 59-95.
    27. Veronesi, Pietro, 1999. "Stock Market Overreaction to Bad News in Good Times: A Rational Expectations Equilibrium Model," Review of Financial Studies, Society for Financial Studies, vol. 12(5), pages 975-1007.
    28. Engle, Robert, 2002. "Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 339-350, July.
    29. Jianhua Gang & Zongxin Qian, 2015. "China’s Monetary Policy and Systemic Risk," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 51(4), pages 701-713, July.
    30. Markose, Sheri & Giansante, Simone & Shaghaghi, Ali Rais, 2012. "‘Too interconnected to fail’ financial network of US CDS market: Topological fragility and systemic risk," Journal of Economic Behavior & Organization, Elsevier, vol. 83(3), pages 627-646.
    31. Mishkin, Frederic S, 1976. "Illiquidity, Consumer Durable Expenditure, and Monetary Policy," American Economic Review, American Economic Association, vol. 66(4), pages 642-654, September.
    32. 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.
    33. 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.
    34. Ravi Bansal & Amir Yaron, 2004. "Risks for the Long Run: A Potential Resolution of Asset Pricing Puzzles," Journal of Finance, American Finance Association, vol. 59(4), pages 1481-1509, August.
    35. Mark J. Flannery & Aris A. Protopapadakis, 2002. "Macroeconomic Factors Do Influence Aggregate Stock Returns," Review of Financial Studies, Society for Financial Studies, vol. 15(3), pages 751-782.
    36. Mobarek, Asma & Muradoglu, Gulnur & Mollah, Sabur & Hou, Ai Jun, 2016. "Determinants of time varying co-movements among international stock markets during crisis and non-crisis periods," Journal of Financial Stability, Elsevier, vol. 24(C), pages 1-11.
    37. Ghysels, Eric & Sinko, Arthur, 2011. "Volatility forecasting and microstructure noise," Journal of Econometrics, Elsevier, vol. 160(1), pages 257-271, January.
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