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Some Results on Measures of Interaction among Risks

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  • Yiting Fan

    (Department of Mathematics, Shantou University, Shantou 515063, China)

  • Rui Fang

    (Department of Mathematics, Shantou University, Shantou 515063, China)

Abstract

It has become a common understanding that financial risk can spread rapidly from one institution to another, and the stressful status of one institution may finally result in a systemic crisis. One popular method to assess and quantify the risk of contagion is employing the co-risk measures and risk contribution measures. It is interesting and important to understand how the underlining dependence structure and magnitude of random risks jointly affect systemic risk measures. In this paper, we mainly focus on the conditional value-at-risk, conditional expected shortfall, the delta conditional value-at-risk, and the delta conditional expected shortfall. Existing studies mainly focus on the situation with two random risks, and this paper makes some contributions by considering the scenario with possibly more than two random risks. By employing the tools of stochastic order, positive dependence concepts and arrangement monotonicity, several results concerning the usual stochastic order, increasing convex order, dispersive order and excess wealth order are presented. Concisely speaking, it is found that for a large enough stress level, a larger random risk tends to lead to a more severe systemic risk. We also performed some Monte Carlo experiments as illustrations for the theoretical findings.

Suggested Citation

  • Yiting Fan & Rui Fang, 2022. "Some Results on Measures of Interaction among Risks," Mathematics, MDPI, vol. 10(19), pages 1-19, October.
  • Handle: RePEc:gam:jmathe:v:10:y:2022:i:19:p:3611-:d:932099
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    1. Miguel Sordo & Héctor Ramos, 2007. "Characterization of stochastic orders by L-functionals," Statistical Papers, Springer, vol. 48(2), pages 249-263, April.
    2. Yamai, Yasuhiro & Yoshiba, Toshinao, 2005. "Value-at-risk versus expected shortfall: A practical perspective," Journal of Banking & Finance, Elsevier, vol. 29(4), pages 997-1015, April.
    3. Righi, Marcelo Brutti & Ceretta, Paulo Sergio, 2015. "A comparison of Expected Shortfall estimation models," Journal of Economics and Business, Elsevier, vol. 78(C), pages 14-47.
    4. Yang, Kun & Wei, Yu & He, Jianmin & Li, Shouwei, 2019. "Dependence and risk spillovers between mainland China and London stock markets before and after the Stock Connect programs," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 526(C).
    5. Chen Zhou, 2010. "Are Banks Too Big to Fail? Measuring Systemic Importance of Financial Institutions," International Journal of Central Banking, International Journal of Central Banking, vol. 6(34), pages 205-250, December.
    6. Yonghong Jiang & Jinqi Mu & He Nie & Lanxin Wu, 2022. "Time‐frequency analysis of risk spillovers from oil to BRICS stock markets: A long‐memory Copula‐CoVaR‐MODWT method," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(3), pages 3386-3404, July.
    7. Alejandro Balbás & José Garrido & Silvia Mayoral, 2009. "Properties of Distortion Risk Measures," Methodology and Computing in Applied Probability, Springer, vol. 11(3), pages 385-399, September.
    8. Kaas, Rob & Laeven, Roger J.A. & Nelsen, Roger B., 2009. "Worst VaR scenarios with given marginals and measures of association," Insurance: Mathematics and Economics, Elsevier, vol. 44(2), pages 146-158, April.
    9. Acerbi, Carlo & Tasche, Dirk, 2002. "On the coherence of expected shortfall," Journal of Banking & Finance, Elsevier, vol. 26(7), pages 1487-1503, July.
    10. Song Xi Chen, 2008. "Nonparametric Estimation of Expected Shortfall," Journal of Financial Econometrics, Oxford University Press, vol. 6(1), pages 87-107, Winter.
    11. Caporin, Massimiliano & Costola, Michele & Garibal, Jean-Charles & Maillet, Bertrand, 2022. "Systemic risk and severe economic downturns: A targeted and sparse analysis," Journal of Banking & Finance, Elsevier, vol. 134(C).
    12. Peter W. Glynn & Yijie Peng & Michael C. Fu & Jian-Qiang Hu, 2021. "Computing Sensitivities for Distortion Risk Measures," INFORMS Journal on Computing, INFORMS, vol. 33(4), pages 1520-1532, October.
    13. Emmanouil N. Karimalis & Nikos K. Nomikos, 2018. "Measuring systemic risk in the European banking sector: a copula CoVaR approach," The European Journal of Finance, Taylor & Francis Journals, vol. 24(11), pages 944-975, July.
    14. Rui Fang & Xiaohu Li, 2018. "Some Results on Measures of Interaction between Paired Risks," Risks, MDPI, vol. 6(3), pages 1-15, August.
    15. Ortega-Jiménez, P. & Sordo, M.A. & Suárez-Llorens, A., 2021. "Stochastic orders and multivariate measures of risk contagion," Insurance: Mathematics and Economics, Elsevier, vol. 96(C), pages 199-207.
    16. Girardi, Giulio & Tolga Ergün, A., 2013. "Systemic risk measurement: Multivariate GARCH estimation of CoVaR," Journal of Banking & Finance, Elsevier, vol. 37(8), pages 3169-3180.
    17. Laeven, Roger J.A., 2009. "Worst VaR scenarios: A remark," Insurance: Mathematics and Economics, Elsevier, vol. 44(2), pages 159-163, April.
    18. Alexander J. McNeil & Rüdiger Frey & Paul Embrechts, 2015. "Quantitative Risk Management: Concepts, Techniques and Tools Revised edition," Economics Books, Princeton University Press, edition 2, number 10496.
    19. Sordo, M.A. & Bello, A.J. & Suárez-Llorens, A., 2018. "Stochastic orders and co-risk measures under positive dependence," Insurance: Mathematics and Economics, Elsevier, vol. 78(C), pages 105-113.
    20. Cai, Jun & Wei, Wei, 2012. "On the invariant properties of notions of positive dependence and copulas under increasing transformations," Insurance: Mathematics and Economics, Elsevier, vol. 50(1), pages 43-49.
    21. Philippe Artzner & Freddy Delbaen & Jean‐Marc Eber & David Heath, 1999. "Coherent Measures of Risk," Mathematical Finance, Wiley Blackwell, vol. 9(3), pages 203-228, July.
    22. Carlo Acerbi & Claudio Nordio & Carlo Sirtori, 2001. "Expected Shortfall as a Tool for Financial Risk Management," Papers cond-mat/0102304, arXiv.org.
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