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FRM: A financial risk meter based on penalizing tail events occurrence

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  • Yu, Lining
  • Härdle, Wolfgang Karl
  • Borke, Lukas
  • Benschop, Thijs

Abstract

In this paper we propose a new measure for systemic risk: the Financial Risk Meter (FRM). This measure is based on the penalization parameter () of a linear quantile lasso regression. The FRM is calculated by taking the average of the penalization parameters over the 100 largest US publicly traded financial institutions. We demonstrate the suitability of this risk measure by comparing the proposed FRM to other measures for systemic risk, such as VIX, SRISK and Google Trends. We find that mutual Granger causality exists between the FRM and these measures, which indicates the validity of the FRM as a systemic risk measure. The implementation of this project is carried out using parallel computing, the codes are published on www.quantlet.de with keyword FRM. The R package RiskAnalytics is another tool with the purpose of integrating and facilitating the research, calculation and analysis methods around the FRM project. The visualization and the up-to-date FRM can be found on http://frm.wiwi.hu-berlin.de.

Suggested Citation

  • Yu, Lining & Härdle, Wolfgang Karl & Borke, Lukas & Benschop, Thijs, 2017. "FRM: A financial risk meter based on penalizing tail events occurrence," SFB 649 Discussion Papers 2017-003, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    • Handle: RePEc:zbw:sfb649:sfb649dp2017-003
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    References listed on IDEAS

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    1. repec:cup:cbooks:9781107034662 is not listed on IDEAS
    2. Borke, Lukas, 2017. "RiskAnalytics: An R package for real time processing of Nasdaq and Yahoo finance data and parallelized quantile lasso regression methods," SFB 649 Discussion Papers 2017-006, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    3. Nikolaus Hautsch & Julia Schaumburg & Melanie Schienle, 2015. "Financial Network Systemic Risk Contributions," Review of Finance, European Finance Association, vol. 19(2), pages 685-738.
    4. Härdle, Wolfgang Karl & Wang, Weining & Yu, Lining, 2016. "TENET: Tail-Event driven NETwork risk," Journal of Econometrics, Elsevier, vol. 192(2), pages 499-513.
    5. Brooks,Chris, 2014. "Introductory Econometrics for Finance," Cambridge Books, Cambridge University Press, number 9781107661455, December.
    6. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
    7. Granger, C. W. J., 1988. "Some recent development in a concept of causality," Journal of Econometrics, Elsevier, vol. 39(1-2), pages 199-211.
    8. Yuan, Ming, 2006. "GACV for quantile smoothing splines," Computational Statistics & Data Analysis, Elsevier, vol. 50(3), pages 813-829, February.
    9. Granger, C. W. J. & Newbold, P., 1974. "Spurious regressions in econometrics," Journal of Econometrics, Elsevier, vol. 2(2), pages 111-120, July.
    10. Kane, Michael & Emerson, John W. & Weston, Stephen, 2013. "Scalable Strategies for Computing with Massive Data," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 55(i14).
    11. Fan, Yan & Härdle, Wolfgang Karl & Wang, Weining & Zhu, Lixing, 2013. "Composite quantile regression for the single-index model," SFB 649 Discussion Papers 2013-010, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    12. Ming Yuan & Yi Lin, 2006. "Model selection and estimation in regression with grouped variables," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(1), pages 49-67, February.
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    Cited by:

    1. Zbonakova, Lenka & Pio Monti, Ricardo & Härdle, Wolfgang Karl, 2018. "Towards the interpretation of time-varying regularization parameters in streaming penalized regression models," IRTG 1792 Discussion Papers 2018-059, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    2. repec:hum:wpaper:sfb649dp2017-006 is not listed on IDEAS
    3. Borke, Lukas, 2017. "RiskAnalytics: An R package for real time processing of Nasdaq and Yahoo finance data and parallelized quantile lasso regression methods," SFB 649 Discussion Papers 2017-006, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    4. Mihoci, Andrija & Althof, Michael & Chen, Cathy Yi-Hsuan & Härdle, Wolfgang Karl, 2019. "FRM Financial Risk Meter," IRTG 1792 Discussion Papers 2019-021, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".

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    More about this item

    Keywords

    Systemic Risk; Quantile Regression; Value at Risk; Lasso; Parallel Computing;
    All these keywords.

    JEL classification:

    • 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
    • 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

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