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Identifying SIFIs: Toward the Simpler Approach

Author

Listed:
  • Sylvain Benoît

    (LEDa - Laboratoire d'Economie de Dauphine - IRD - Institut de Recherche pour le Développement - Université Paris Dauphine-PSL - PSL - Université Paris Sciences et Lettres - CNRS - Centre National de la Recherche Scientifique)

  • Jeremy Dudek

    (CREST - Centre de Recherche en Économie et Statistique - ENSAI - Ecole Nationale de la Statistique et de l'Analyse de l'Information [Bruz] - X - École polytechnique - IP Paris - Institut Polytechnique de Paris - ENSAE Paris - École Nationale de la Statistique et de l'Administration Économique - CNRS - Centre National de la Recherche Scientifique)

  • Manizha Sharifova

    (UC Santa Cruz - University of California [Santa Cruz] - UC - University of California)

Abstract

Systemic risk measures generally aim to identify systemically important financial institutions (SIFIs) that would allow regulators to allocate macro-prudential capital requirements in order to reduce risk stemming from such institutions. Among widely-cited are the measures of tail dependence in financial institutions' equity returns, such as ΔCoVaR of Adrian and Brunnermeier (2011) and Marginal Expected Shortfall (MES) of Acharya et al. (2010). This paper compares nonlinear and linear approaches to modeling return dependence in the estimation of the ΔCoVaR and MES. Our results show that while the refined and complicated estimation techniques are able to produce more accurate value of institution's systemic risk contribution they do not greatly improve in terms of identifying SIFIs compared to simpler linear estimation method. Modeling dependence linearly sufficient to identify and rank SIFIs.

Suggested Citation

  • Sylvain Benoît & Jeremy Dudek & Manizha Sharifova, 2017. "Identifying SIFIs: Toward the Simpler Approach," Working Papers hal-01500426, HAL.
    • Handle: RePEc:hal:wpaper:hal-01500426
    as

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