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CoCDaR and mCoCDaR: New Approach for Measurement of Systemic Risk Contributions

Author

Listed:
  • Rui Ding

    (Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY 11794, USA)

  • Stan Uryasev

    (Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY 11794, USA)

Abstract

Systemic risk is the risk that the distress of one or more institutions trigger a collapse of the entire financial system. We extend CoVaR (value-at-risk conditioned on an institution) and CoCVaR (conditional value-at-risk conditioned on an institution) systemic risk contribution measures and propose a new CoCDaR (conditional drawdown-at-risk conditioned on an institution) measure based on drawdowns. This new measure accounts for consecutive negative returns of a security, while CoVaR and CoCVaR combine together negative returns from different time periods. For instance, ten 2% consecutive losses resulting in 20% drawdown will be noticed by CoCDaR, while CoVaR and CoCVaR are not sensitive to relatively small one period losses. The proposed measure provides insights for systemic risks under extreme stresses related to drawdowns. CoCDaR and its multivariate version, mCoCDaR, estimate an impact on big cumulative losses of the entire financial system caused by an individual firm’s distress. It can be used for ranking individual systemic risk contributions of financial institutions (banks). CoCDaR and mCoCDaR are computed with CVaR regression of drawdowns. Moreover, mCoCDaR can be used to estimate drawdowns of a security as a function of some other factors. For instance, we show how to perform fund drawdown style classification depending on drawdowns of indices. Case study results, data, and codes are posted on the web.

Suggested Citation

  • Rui Ding & Stan Uryasev, 2020. "CoCDaR and mCoCDaR: New Approach for Measurement of Systemic Risk Contributions," JRFM, MDPI, vol. 13(11), pages 1-18, November.
    • Handle: RePEc:gam:jjrfmx:v:13:y:2020:i:11:p:270-:d:439333
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    References listed on IDEAS

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    1. Alexei Chekhlov & Stanislav Uryasev & Michael Zabarankin, 2005. "Drawdown Measure In Portfolio Optimization," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 8(01), pages 13-58.
    2. Alex Golodnikov & Viktor Kuzmenko & Stan Uryasev, 2019. "CVaR Regression Based on the Relation between CVaR and Mixed-Quantile Quadrangles," JRFM, MDPI, vol. 12(3), pages 1-22, June.
    3. Mauro Bernardi & Lea Petrella, 2015. "Interconnected Risk Contributions: A Heavy-Tail Approach to Analyze U.S. Financial Sectors," JRFM, MDPI, vol. 8(2), pages 1-29, April.
    4. Carhart, Mark M, 1997. "On Persistence in Mutual Fund Performance," Journal of Finance, American Finance Association, vol. 52(1), pages 57-82, March.
    5. Rockafellar, R.T. & Royset, J.O. & Miranda, S.I., 2014. "Superquantile regression with applications to buffered reliability, uncertainty quantification, and conditional value-at-risk," European Journal of Operational Research, Elsevier, vol. 234(1), pages 140-154.
    6. Zabarankin, Michael & Pavlikov, Konstantin & Uryasev, Stan, 2014. "Capital Asset Pricing Model (CAPM) with drawdown measure," European Journal of Operational Research, Elsevier, vol. 234(2), pages 508-517.
    7. Gilbert W. Bassett Jr. & Hsiu-Lang Chen, 2001. "Portfolio style: Return-based attribution using quantile regression," Empirical Economics, Springer, vol. 26(1), pages 293-305.
    8. Rockafellar, R. Tyrrell & Uryasev, Stanislav, 2002. "Conditional value-at-risk for general loss distributions," Journal of Banking & Finance, Elsevier, vol. 26(7), pages 1443-1471, July.
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    Cited by:

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