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TENET: Tail-Event driven NETwork risk

Author

Listed:
  • Wolfgang Karl Härdle
  • Natalia Sirotko-Sibirskaya
  • Weining Wang

Abstract

We propose a semiparametric measure to estimate systemic interconnectedness across financial institutions based on tail-driven spill-over effects in a ultra-high dimensional framework. Methodologically, we employ a variable selection technique in a time series setting in the context of a single-index model for a generalized quantile regression framework. We can thus include more financial institutions into the analysis, to measure their interdependencies in tails and, at the same time, to take into account non-linear relationships between them. A empirical application on a set of 200 publicly traded U. S. nancial institutions provides useful rankings of systemic exposure and systemic contribution at various stages of financial crisis. Network analysis, its behaviour and dynamics, allows us to characterize a role of each sector in the financial crisis and yields a new perspective of the nancial markets at the U. S. financial market 2007 - 2012.

Suggested Citation

  • Wolfgang Karl Härdle & Natalia Sirotko-Sibirskaya & Weining Wang, 2014. "TENET: Tail-Event driven NETwork risk," SFB 649 Discussion Papers SFB649DP2014-066, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
  • Handle: RePEc:hum:wpaper:sfb649dp2014-066
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Zbonakova, L. & Härdle, W.K. & Wang, W., 2016. "Time Varying Quantile Lasso," Working Papers 16/07, Department of Economics, City University London.
    2. Ya Qian & Wolfgang Karl Härdle & Cathy Yi-Hsuan Chen, 2017. "Industry Interdependency Dynamics in a Network Context," SFB 649 Discussion Papers SFB649DP2017-012, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    3. Cathy Yi-Hsuan Chen & Wolfgang Karl Härdle & Yarema Okhrin, 2017. "Tail event driven networks of SIFIs," SFB 649 Discussion Papers SFB649DP2017-004, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    4. Xinjue Li & Lenka Zbonakova & Wolfgang Karl Härdle, 2017. "Penalized Adaptive Method in Forecasting with Large Information Set and Structure Change," SFB 649 Discussion Papers SFB649DP2017-023, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    5. Bernardi, M. & Durante, F. & Jaworski, P., 2017. "CoVaR of families of copulas," Statistics & Probability Letters, Elsevier, vol. 120(C), pages 8-17.
    6. Li Guo & Yubo Tao & Jun Tu, 2017. "Media Network and Return Predictability," Papers 1703.02715, arXiv.org, revised Dec 2017.
    7. Chernozhukov, V. & Härdle, W.K. & Huang, C. & Wang, W., 2018. "LASSO-Driven Inference in Time and Space," Working Papers 18/04, Department of Economics, City University London.
    8. Lining Yu & Wolfgang Karl Härdle & Lukas Borke & Thijs Benschop, 2017. "FRM: a Financial Risk Meter based on penalizing tail events occurrence," SFB 649 Discussion Papers SFB649DP2017-003, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    9. repec:eee:ememar:v:35:y:2018:i:c:p:1-18 is not listed on IDEAS
    10. Sullivan HUE & Yannick LUCOTTE & Sessi TOKPAVI, 2018. "Measuring Network Systemic Risk Contributions: A Leave-one-out Approach," LEO Working Papers / DR LEO 2608, Orleans Economics Laboratory / Laboratoire d'Economie d'Orleans (LEO), University of Orleans.
    11. repec:eee:ememar:v:35:y:2018:i:c:p:190-206 is not listed on IDEAS
    12. Dingshi Tian & Zongwu Cai & Ying Fang, 2018. "Econometric Modeling of Risk Measures: A Selective Review of the Recent Literature," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 201807, University of Kansas, Department of Economics, revised Oct 2018.
    13. Silva, Walmir & Kimura, Herbert & Sobreiro, Vinicius Amorim, 2017. "An analysis of the literature on systemic financial risk: A survey," Journal of Financial Stability, Elsevier, vol. 28(C), pages 91-114.

    More about this item

    Keywords

    Systemic Risk; Systemic Risk Network; Generalized Quantile; Quantile Single-Index Regression; Value at Risk; CoVaR; Lasso;

    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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