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Granger causality and systemic risk

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  • Balboa, Marina
  • López-Espinosa, Germán
  • Rubia, Antonio

Abstract

Building on the concept of Granger causality in risk in Hong et al. (2009), and focusing on an international sample of large-capitalization banks, we test for predictability in comovements in the left tails of returns of individual banks and the global system. The main results show that large individual shocks (defined as balance-sheet contractions exceeding the 1% VaR level) are a strong predictor of subsequent shocks in the global system. This evidence is particularly strong for US banks with large desks of proprietary trading. Similarly, we document strong evidence of financial vulnerabilities (exposures) to systemic shocks in US subprime creditors.

Suggested Citation

  • Balboa, Marina & López-Espinosa, Germán & Rubia, Antonio, 2015. "Granger causality and systemic risk," Finance Research Letters, Elsevier, vol. 15(C), pages 49-58.
    • Handle: RePEc:eee:finlet:v:15:y:2015:i:c:p:49-58
    DOI: 10.1016/j.frl.2015.08.003
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    References listed on IDEAS

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    1. Alemany, Aida & Ballester, Laura & González-Urteaga, Ana, 2015. "Volatility spillovers in the European bank CDS market," Finance Research Letters, Elsevier, vol. 13(C), pages 137-147.
    2. 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.
    3. López-Espinosa, Germán & Moreno, Antonio & Rubia, Antonio & Valderrama, Laura, 2012. "Short-term wholesale funding and systemic risk: A global CoVaR approach," Journal of Banking & Finance, Elsevier, vol. 36(12), pages 3150-3162.
    4. Engle, Robert F & Manganelli, Simone, 1999. "CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles," University of California at San Diego, Economics Working Paper Series qt06m3d6nv, Department of Economics, UC San Diego.
    5. Hong, Yongmiao & Liu, Yanhui & Wang, Shouyang, 2009. "Granger causality in risk and detection of extreme risk spillover between financial markets," Journal of Econometrics, Elsevier, vol. 150(2), pages 271-287, June.
    6. 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.
    7. 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.
    8. Robert F. Engle & Simone Manganelli, 2004. "CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles," Journal of Business & Economic Statistics, American Statistical Association, vol. 22, pages 367-381, October.
    9. International Monetary Fund, 2012. "Short-Term Wholesale Funding and Systemic Risk: A Global Covar Approach," IMF Working Papers 2012/046, International Monetary Fund.
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    Cited by:

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    3. Wang, Gang-Jin & Xie, Chi & Jiang, Zhi-Qiang & Stanley, H. Eugene, 2016. "Extreme risk spillover effects in world gold markets and the global financial crisis," International Review of Economics & Finance, Elsevier, vol. 46(C), pages 55-77.
    4. Cincinelli, Peter & Pellini, Elisabetta & Urga, Giovanni, 2022. "Systemic risk in the Chinese financial system: A panel Granger causality analysis," International Review of Financial Analysis, Elsevier, vol. 82(C).
    5. Qunwei Wang & Xingyu Dai & Dequn Zhou, 2020. "Dynamic Correlation and Risk Contagion Between “Black” Futures in China: A Multi-scale Variational Mode Decomposition Approach," Computational Economics, Springer;Society for Computational Economics, vol. 55(4), pages 1117-1150, April.
    6. Omid Farkhondeh Rouz & Hossein Sohrabi Vafa & Arash Sioofy Khoojine & Sajjad Pashay Amiri, 2024. "Interconnectedness of systemic risk in the Chinese economy: the Granger causality and CISS indicator approach," Risk Management, Palgrave Macmillan, vol. 26(2), pages 1-24, May.
    7. Hasan Hanif & Muhammad Naveed & David McMillan, 2020. "Dynamic modeling of idiosyncratic risk under economic sensitivity. A case of Pakistan," Cogent Economics & Finance, Taylor & Francis Journals, vol. 8(1), pages 1838734-183, January.

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    More about this item

    Keywords

    Interconnection; Spillover; Financial contagion;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • G01 - Financial Economics - - General - - - Financial Crises
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages

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