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The information in systemic risk rankings

Author

Listed:
  • Nucera, Federico
  • Schwaab, Bernd
  • Koopman, Siem Jan
  • Lucas, André

Abstract

We propose to pool alternative systemic risk rankings for financial institutions using the method of principal components. The resulting overall ranking is less affected by estimation uncertainty and model risk. We apply our methodology to disentangle the common signal and the idiosyncratic components from a selection of key systemic risk rankings that have been proposed recently. We use a sample of 113 listed financial sector firms in the European Union over the period 2002–2013. The implied ranking from the principal components is less volatile than most individual risk rankings and leads to less turnover among the top ranked institutions. We also find that price-based rankings and fundamentals-based rankings deviated substantially and for a prolonged time in the period leading up to the financial crisis. We test the adequacy of our newly pooled systemic risk ranking by relating it to credit default swap premia.

Suggested Citation

  • Nucera, Federico & Schwaab, Bernd & Koopman, Siem Jan & Lucas, André, 2016. "The information in systemic risk rankings," Journal of Empirical Finance, Elsevier, vol. 38(PA), pages 461-475.
  • Handle: RePEc:eee:empfin:v:38:y:2016:i:pa:p:461-475
    DOI: 10.1016/j.jempfin.2016.01.002
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    References listed on IDEAS

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    Cited by:

    1. repec:spr:annopr:v:262:y:2018:i:2:d:10.1007_s10479-016-2113-8 is not listed on IDEAS
    2. van de Leur, Michiel C.W. & Lucas, André & Seeger, Norman J., 2017. "Network, market, and book-based systemic risk rankings," Journal of Banking & Finance, Elsevier, vol. 78(C), pages 84-90.
    3. Antonio Di Cesare & Anna Rogantini Picco, 2018. "A Survey of Systemic Risk Indicators," Questioni di Economia e Finanza (Occasional Papers) 458, Bank of Italy, Economic Research and International Relations Area.
    4. repec:cup:jfinqa:v:53:y:2018:i:03:p:1371-1390_00 is not listed on IDEAS
    5. Kräussl, Roman & Lehnert, Thorsten & Stefanova, Denitsa, 2016. "The European sovereign debt crisis: What have we learned?," Journal of Empirical Finance, Elsevier, vol. 38(PA), pages 363-373.
    6. repec:eee:ecolet:v:174:y:2019:i:c:p:173-178 is not listed on IDEAS
    7. Geraci, Marco Valerio & Gnabo, Jean-Yves, 2018. "Measuring Interconnectedness between Financial Institutions with Bayesian Time-Varying Vector Autoregressions," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 53(03), pages 1371-1390, June.
    8. repec:kap:rqfnac:v:52:y:2019:i:4:d:10.1007_s11156-018-0732-7 is not listed on IDEAS
    9. Jokivuolle, Esa & Tunaru, Radu & Vioto, Davide, 2018. "Testing the systemic risk differences in banks," Research Discussion Papers 13/2018, Bank of Finland.
    10. repec:eee:empfin:v:50:y:2019:i:c:p:1-19 is not listed on IDEAS
    11. Abendschein, Michael & Grundke, Peter, 2018. "On the ranking consistency of global systemic risk measures: empirical evidence," Annual Conference 2018 (Freiburg, Breisgau): Digital Economy 181623, Verein für Socialpolitik / German Economic Association.
    12. Marco Valerio Geraci & Jean-Yves Gnabo, 2015. "Measuring Interconnectedness between Financial Institutions with Bayesian Time-Varying VARS," Working Papers ECARES ECARES 2015-51, ULB -- Universite Libre de Bruxelles.

    More about this item

    Keywords

    Systemic risk contribution; Risk rankings; Forecast combination; Financial regulation; Banking supervision;

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • 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
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications

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