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Financial network systemic risk contributions

Author

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  • Hautsch, Nikolaus
  • Schaumburg, Julia
  • Schienle, Melanie

Abstract

We propose the realized systemic risk beta as a measure for financial companies' contribution to systemic risk given network interdependence between firms' tail risk exposures. Conditional on statistically pre-identified network spillover effects and market and balance sheet information, we define the realized systemic risk beta as the total time-varying marginal effect of a firm's Value-at-risk (VaR) on the system's VaR. Suitable statistical inference reveals a multitude of relevant risk spillover channels and determines companies' systemic importance in the U.S. financial system. Our approach can be used to monitor companies' systemic importance allowing for a transparent macroprudential regulation.

Suggested Citation

  • Hautsch, Nikolaus & Schaumburg, Julia & Schienle, Melanie, 2012. "Financial network systemic risk contributions," SFB 649 Discussion Papers 2012-053, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
  • Handle: RePEc:zbw:sfb649:sfb649dp2012-053
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    References listed on IDEAS

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    JEL classification:

    • G01 - Financial Economics - - General - - - Financial Crises
    • G18 - Financial Economics - - General Financial Markets - - - Government Policy and Regulation
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
    • G38 - Financial Economics - - Corporate Finance and Governance - - - Government Policy and Regulation
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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