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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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    3. 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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