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Identifying systemic important markets from a global perspective: Using the ADCC ΔCoVaR approach with skewed-t distribution

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  • Fang, Libing
  • Chen, Baizhu
  • Yu, Honghai
  • Qian, Yichuo

Abstract

The objective of this paper is to evaluate the risk contributions of G7 and BRICS stock markets based on the Asymmetric Dynamic Conditional Correlation (ADCC) Delta Conditional Value at Risk (ΔCoVaR) measurement with skewed-t distribution. Our empirical results reveal that developed markets contribute relatively more to global systemic risk than emerging markets. Notably, among all markets, Brazil is second only to the US for contributing the most risk to the global system during periods of distress. Conversely, Japan contributed the least amount of systemic risk. The results of this study can significantly help the entire community of researchers and security regulators in monitoring systemic risk and promoting financial stability.

Suggested Citation

  • Fang, Libing & Chen, Baizhu & Yu, Honghai & Qian, Yichuo, 2018. "Identifying systemic important markets from a global perspective: Using the ADCC ΔCoVaR approach with skewed-t distribution," Finance Research Letters, Elsevier, vol. 24(C), pages 137-144.
    • Handle: RePEc:eee:finlet:v:24:y:2018:i:c:p:137-144
    DOI: 10.1016/j.frl.2017.08.002
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    1. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    2. Yang, Hsin-Feng & Liu, Chih-Liang & Chou, Ray Yeutien, 2014. "Interest rate risk propagation: Evidence from the credit crunch," The North American Journal of Economics and Finance, Elsevier, vol. 28(C), pages 242-264.
    3. Bauwens, Luc & Laurent, Sebastien, 2005. "A New Class of Multivariate Skew Densities, With Application to Generalized Autoregressive Conditional Heteroscedasticity Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 346-354, July.
    4. Lorenzo Cappiello & Robert F. Engle & Kevin Sheppard, 2006. "Asymmetric Dynamics in the Correlations of Global Equity and Bond Returns," Journal of Financial Econometrics, Oxford University Press, vol. 4(4), pages 537-572.
    5. Bali, Turan G. & Mo, Hengyong & Tang, Yi, 2008. "The role of autoregressive conditional skewness and kurtosis in the estimation of conditional VaR," Journal of Banking & Finance, Elsevier, vol. 32(2), pages 269-282, February.
    6. De Nicolò, Gianni & Juvenal, Luciana, 2014. "Financial integration, globalization, and real activity," Journal of Financial Stability, Elsevier, vol. 10(C), pages 65-75.
    7. Tobias Adrian & Markus K. Brunnermeier, 2016. "CoVaR," American Economic Review, American Economic Association, vol. 106(7), pages 1705-1741, July.
      • Tobias Adrian & Markus K. Brunnermeier, 2008. "CoVaR," Staff Reports 348, Federal Reserve Bank of New York.
      • Tobias Adrian & Markus K. Brunnermeier, 2011. "CoVaR," NBER Working Papers 17454, National Bureau of Economic Research, Inc.
    8. Peia, Oana & Roszbach, Kasper, 2015. "Finance and growth: Time series evidence on causality," Journal of Financial Stability, Elsevier, vol. 19(C), pages 105-118.
    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. Pagano, Michael S. & Sedunov, John, 2016. "A comprehensive approach to measuring the relation between systemic risk exposure and sovereign debt," Journal of Financial Stability, Elsevier, vol. 23(C), pages 62-78.
    11. López-Espinosa, Germán & Moreno, Antonio & Rubia, Antonio & Valderrama, Laura, 2015. "Systemic risk and asymmetric responses in the financial industry," Journal of Banking & Finance, Elsevier, vol. 58(C), pages 471-485.
    12. 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.
    13. 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.
    14. 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.
    15. Dimitriou, Dimitrios & Kenourgios, Dimitris & Simos, Theodore, 2013. "Global financial crisis and emerging stock market contagion: A multivariate FIAPARCH–DCC approach," International Review of Financial Analysis, Elsevier, vol. 30(C), pages 46-56.
    16. Bellavite Pellegrini, Carlo & Meoli, Michele & Urga, Giovanni, 2017. "Money market funds, shadow banking and systemic risk in United Kingdom," Finance Research Letters, Elsevier, vol. 21(C), pages 163-171.
    17. Kenourgios, Dimitris & Samitas, Aristeidis & Paltalidis, Nikos, 2011. "Financial crises and stock market contagion in a multivariate time-varying asymmetric framework," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 21(1), pages 92-106, February.
    18. López-Espinosa, Germán & Rubia, Antonio & Valderrama, Laura & Antón, Miguel, 2013. "Good for one, bad for all: Determinants of individual versus systemic risk," Journal of Financial Stability, Elsevier, vol. 9(3), pages 287-299.
    19. Mikhail Stolbov, 2017. "Assessing systemic risk and its determinants for advanced and major emerging economies: the case of ΔCoVaR," International Economics and Economic Policy, Springer, vol. 14(1), pages 119-152, January.
    20. Ribeiro, Pedro Pires & Cermeño, Rodolfo & Curto, José Dias, 2017. "Sovereign bond markets and financial volatility dynamics: Panel-GARCH evidence for six euro area countries," Finance Research Letters, Elsevier, vol. 21(C), pages 107-114.
    21. 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.
    22. Drakos, Anastassios A. & Kouretas, Georgios P., 2015. "Bank ownership, financial segments and the measurement of systemic risk: An application of CoVaR," International Review of Economics & Finance, Elsevier, vol. 40(C), pages 127-140.
    23. 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.
    24. Tai, Chu-Sheng, 2007. "Market integration and contagion: Evidence from Asian emerging stock and foreign exchange markets," Emerging Markets Review, Elsevier, vol. 8(4), pages 264-283, December.
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    Cited by:

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    2. Xu, Qifa & Li, Mengting & Jiang, Cuixia & He, Yaoyao, 2019. "Interconnectedness and systemic risk network of Chinese financial institutions: A LASSO-CoVaR approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).
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    More about this item

    Keywords

    Systemic risk contribution; Global financial crisis; ADCC; CoVaR;
    All these keywords.

    JEL classification:

    • C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General
    • C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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