ISSN 0253-2778

CN 34-1054/N

Open AccessOpen Access JUSTC Research Reviews: Management Science and Engineering

Measuring systemic risk contribution with CoGVaR approach

Cite this:
https://doi.org/10.52396/JUST-2021-0140
  • Received Date: 27 May 2021
  • Rev Recd Date: 31 May 2021
  • Publish Date: 30 June 2021
  • We propose a new conditional risk measure, conditional generalized value-at-risk (CoGVaR), from the perspective of measuring systemic risk. The new class of risk measures is a natural generalization of the conditional quantiles including the classic CoVaR. Compared with the classic conditional value-at-risk (CoVaR) and conditional expectile (CoExpectile), it has more potential application in reality as it takes the risk attitude of the decision maker into consideration, which has not been the focus of much study to date. Using generalized quantile regression approach with state variables added, some calculation results are presented in the Dow Jones U.S. Financials Index case, and it is shown that it provides a new perspective on systemic risk contribution. In addition, the result shows that our risk measure can capture the tail risk by using more convex disutility function.
    We propose a new conditional risk measure, conditional generalized value-at-risk (CoGVaR), from the perspective of measuring systemic risk. The new class of risk measures is a natural generalization of the conditional quantiles including the classic CoVaR. Compared with the classic conditional value-at-risk (CoVaR) and conditional expectile (CoExpectile), it has more potential application in reality as it takes the risk attitude of the decision maker into consideration, which has not been the focus of much study to date. Using generalized quantile regression approach with state variables added, some calculation results are presented in the Dow Jones U.S. Financials Index case, and it is shown that it provides a new perspective on systemic risk contribution. In addition, the result shows that our risk measure can capture the tail risk by using more convex disutility function.
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  • [1]
    Adrian T, Brunnermeier M K. CoVaR. American Economic Review, 2016, 106(7): 1705-1741.
    [2]
    Girardi G, Ergün A T. Systemic risk measurement: Multivariate GARCH estimation of CoVaR. Journal of Banking & Finance,2013, 37(8): 3169-3180.
    [3]
    Huang W Q, Uryasev S. The CoCVaR approach: Systemic risk contribution measurement. Journal of Risk, 2018, 20(4): 75-93.
    [4]
    Brownlees C, Engle R F. SRISK: A conditional capital shortfall measure of systemic risk. The Review of Financial Studies, 2017, 30(1): 48-79.
    [5]
    Acharya V V, Pedersen L H, Philippon T, et al. Measuring systemic risk. The Review of Financial Studies, 2017, 30(1): 2-47.
    [6]
    Bellini F, Klar B, Müller A, et al. Generalized quantiles as risk measures. Insurance: Mathematics and Economics, 2014, 54: 41-48.
    [7]
    Newey W K, Powell J L. Asymmetric least squares estimation and testing. Econometrica: Journal of the Econometric Society, 1987, 55(4): 819-847.
    [8]
    Föllmer H, Schied A. Stochastic Finance: An Introduction in Discrete Time. Fourth edition. Berlin: Walter de Gruyter, 2016.
    [9]
    Rockafellar R T, Royset J O, Miranda S I. Superquantile regression withapplications to buffered reliability, uncertainty quantification, and conditional value-at-risk. European Journal of Operational Research, 2014, 234(1): 140-154.
    [10]
    Rockafellar R T, Uryasev S. The fundamental risk quadrangle in risk management, optimization and statistical estimation. Surveys in Operations Research and Management Science, 2013, 18(1-2): 33-53.
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Catalog

    [1]
    Adrian T, Brunnermeier M K. CoVaR. American Economic Review, 2016, 106(7): 1705-1741.
    [2]
    Girardi G, Ergün A T. Systemic risk measurement: Multivariate GARCH estimation of CoVaR. Journal of Banking & Finance,2013, 37(8): 3169-3180.
    [3]
    Huang W Q, Uryasev S. The CoCVaR approach: Systemic risk contribution measurement. Journal of Risk, 2018, 20(4): 75-93.
    [4]
    Brownlees C, Engle R F. SRISK: A conditional capital shortfall measure of systemic risk. The Review of Financial Studies, 2017, 30(1): 48-79.
    [5]
    Acharya V V, Pedersen L H, Philippon T, et al. Measuring systemic risk. The Review of Financial Studies, 2017, 30(1): 2-47.
    [6]
    Bellini F, Klar B, Müller A, et al. Generalized quantiles as risk measures. Insurance: Mathematics and Economics, 2014, 54: 41-48.
    [7]
    Newey W K, Powell J L. Asymmetric least squares estimation and testing. Econometrica: Journal of the Econometric Society, 1987, 55(4): 819-847.
    [8]
    Föllmer H, Schied A. Stochastic Finance: An Introduction in Discrete Time. Fourth edition. Berlin: Walter de Gruyter, 2016.
    [9]
    Rockafellar R T, Royset J O, Miranda S I. Superquantile regression withapplications to buffered reliability, uncertainty quantification, and conditional value-at-risk. European Journal of Operational Research, 2014, 234(1): 140-154.
    [10]
    Rockafellar R T, Uryasev S. The fundamental risk quadrangle in risk management, optimization and statistical estimation. Surveys in Operations Research and Management Science, 2013, 18(1-2): 33-53.

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