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Exploring Nonlinearities in Financial Systemic Risk

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  • Wolski, M.

    () (University of Amsterdam)

Abstract

We propose a new methodology of assessing the effects of individual institution's risk on the others and on the system as a whole. We build upon the Conditional Value-at-Risk approach, however, we introduce the explicit Granger causal linkages and we account for possible nonlinearities in the financial time series. Conditional Value-at-Risk-Nonlinear Granger Causality, or NCoVaR as we call it, has regular asymptotic properties which makes it particulary appealing for practical applications. We test our approach empirically and assess the contribution of the euro area financial companies to the overall systemic risk. We find that only a few financial institutions pose a serious ex ante threat to the systemic risk, whereas, given that the system is already in trouble, there are more institutions which hamper its recovery. Moreover, we discover non-negligible nonlinear structures in the systemic risk profile of the euro zone.

Suggested Citation

  • Wolski, M., 2013. "Exploring Nonlinearities in Financial Systemic Risk," CeNDEF Working Papers 13-14, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
  • Handle: RePEc:ams:ndfwpp:13-14
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    References listed on IDEAS

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    1. Acharya, Viral V., 2009. "A theory of systemic risk and design of prudential bank regulation," Journal of Financial Stability, Elsevier, vol. 5(3), pages 224-255, September.
    2. M. Jones, 1992. "Estimating densities, quantiles, quantile densities and density quantiles," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 44(4), pages 721-727, December.
    3. repec:wly:japmet:v:31:y:2016:i:7:p:1333-1351 is not listed on IDEAS
    4. Francis, Bill B. & Mougoué, Mbodja & Panchenko, Valentyn, 2010. "Is there a symmetric nonlinear causal relationship between large and small firms?," Journal of Empirical Finance, Elsevier, vol. 17(1), pages 23-38, January.
    5. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 33(1), pages 125-132.
    6. Cees Diks & Marcin Wolski, 2016. "Nonlinear Granger Causality: Guidelines for Multivariate Analysis," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(7), pages 1333-1351, November.
    7. Diks, Cees & Panchenko, Valentyn, 2006. "A new statistic and practical guidelines for nonparametric Granger causality testing," Journal of Economic Dynamics and Control, Elsevier, vol. 30(9-10), pages 1647-1669.
    8. Granger, C W J, 1969. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, Econometric Society, vol. 37(3), pages 424-438, July.
    9. Markus K. Brunnermeier, 2009. "Deciphering the Liquidity and Credit Crunch 2007-2008," Journal of Economic Perspectives, American Economic Association, vol. 23(1), pages 77-100, Winter.
    10. Hiemstra, Craig & Jones, Jonathan D, 1994. " Testing for Linear and Nonlinear Granger Causality in the Stock Price-Volume Relation," Journal of Finance, American Finance Association, vol. 49(5), pages 1639-1664, December.
    11. 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.
    12. Peter Hall & Michael C. Minnotte, 2002. "High order data sharpening for density estimation," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(1), pages 141-157.
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