IDEAS home Printed from https://ideas.repec.org/a/bla/jrinsu/v73y2006i2p193-229.html
   My bibliography  Save this article

After VaR: The Theory, Estimation, and Insurance Applications of Quantile‐Based Risk Measures

Author

Listed:
  • Kevin Dowd
  • David Blake

Abstract

We discuss a number of quantile‐based risk measures (QBRMs) that have recently been developed in the financial risk and actuarial/insurance literatures. The measures considered include the Value‐at‐Risk (VaR), coherent risk measures, spectral risk measures, and distortion risk measures. We discuss and compare the properties of these different measures, and point out that the VaR is seriously flawed. We then discuss how QBRMs can be estimated, and discuss some of the many ways they might be applied to insurance risk problems. These applications are typically very complex, and this complexity means that the most appropriate estimation method will often be some form of stochastic simulation.

Suggested Citation

  • Kevin Dowd & David Blake, 2006. "After VaR: The Theory, Estimation, and Insurance Applications of Quantile‐Based Risk Measures," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 73(2), pages 193-229, June.
    • Handle: RePEc:bla:jrinsu:v:73:y:2006:i:2:p:193-229
    DOI: 10.1111/j.1539-6975.2006.00171.x
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/j.1539-6975.2006.00171.x
    Download Restriction: no
    File URL: https://libkey.io/10.1111/j.1539-6975.2006.00171.x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Goovaerts, Marc J. & Kaas, Rob & Dhaene, Jan & Tang, Qihe, 2003. "A Unified Approach to Generate Risk Measures," ASTIN Bulletin, Cambridge University Press, vol. 33(2), pages 173-191, November.
    2. Kato, Toshiyasu & Yoshiba, Toshinao, 2000. "Model Risk and Its Control," Monetary and Economic Studies, Institute for Monetary and Economic Studies, Bank of Japan, vol. 18(2), pages 129-157, December.
    3. Renato Pelessoni & Paolo Vicig, 2002. "Coherent Risk Measures and Upper Previsions," Risk and Insurance 0201001, University Library of Munich, Germany.
    4. Tripp, Michael Howard & Bradley, H. L. & Devitt, R. & Orros, G. C. & Overton, G. L. & Pryor, L. M. & Shaw, R. A., 2004. "Quantifying Operational Risk in General Insurance Companies. Developed by a Giro Working Party," British Actuarial Journal, Cambridge University Press, vol. 10(5), pages 919-1012, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Goovaerts, Marc J. & Kaas, Rob & Dhaene, Jan & Tang, Qihe, 2004. "Some new classes of consistent risk measures," Insurance: Mathematics and Economics, Elsevier, vol. 34(3), pages 505-516, June.
    2. Rosazza Gianin, Emanuela, 2006. "Risk measures via g-expectations," Insurance: Mathematics and Economics, Elsevier, vol. 39(1), pages 19-34, August.
    3. Goovaerts, Marc & Linders, Daniël & Van Weert, Koen & Tank, Fatih, 2012. "On the interplay between distortion, mean value and Haezendonck–Goovaerts risk measures," Insurance: Mathematics and Economics, Elsevier, vol. 51(1), pages 10-18.
    4. René Doff, 2016. "The Final Solvency II Framework: Will It Be Effective?," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 41(4), pages 587-607, October.
    5. Carol Alexander & Jose Maria Sarabia, 2010. "Endogenizing Model Risk to Quantile Estimates," ICMA Centre Discussion Papers in Finance icma-dp2010-07, Henley Business School, University of Reading.
    6. Major, John A., 2018. "Distortion measures and homogeneous financial derivatives," Insurance: Mathematics and Economics, Elsevier, vol. 79(C), pages 82-91.
    7. Bellini, Fabio & Rosazza Gianin, Emanuela, 2008. "On Haezendonck risk measures," Journal of Banking & Finance, Elsevier, vol. 32(6), pages 986-994, June.
    8. Johannes Leitner, 2008. "Fair (intra-bank transfer) prices for credits with stochastic recovery," Annals of Finance, Springer, vol. 4(2), pages 243-253, March.
    9. Goovaerts, Marc J. & Kaas, Rob & Laeven, Roger J.A. & Tang, Qihe, 2004. "A comonotonic image of independence for additive risk measures," Insurance: Mathematics and Economics, Elsevier, vol. 35(3), pages 581-594, December.
    10. Claude Lefèvre & Stéphane Loisel, 2013. "On multiply monotone distributions, continuous or discrete, with applications," Post-Print hal-00750562, HAL.
    11. Roberto Cominetti & Alfredo Torrico, 2016. "Additive Consistency of Risk Measures and Its Application to Risk-Averse Routing in Networks," Mathematics of Operations Research, INFORMS, vol. 41(4), pages 1510-1521, November.
    12. Stadje, Mitja, 2010. "Extending dynamic convex risk measures from discrete time to continuous time: A convergence approach," Insurance: Mathematics and Economics, Elsevier, vol. 47(3), pages 391-404, December.
    13. Frank Fabozzi & Radu Tunaru, 2006. "On risk management problems related to a coherence property," Quantitative Finance, Taylor & Francis Journals, vol. 6(1), pages 75-81.
    14. Giannopoulos, Kostas & Tunaru, Radu, 2005. "Coherent risk measures under filtered historical simulation," Journal of Banking & Finance, Elsevier, vol. 29(4), pages 979-996, April.
    15. Goovaerts, Marc J. & Kaas, Rob & Laeven, Roger J.A., 2011. "Worst case risk measurement: Back to the future?," Insurance: Mathematics and Economics, Elsevier, vol. 49(3), pages 380-392.
    16. Ahn, Jae Youn & Shyamalkumar, Nariankadu D., 2014. "Asymptotic theory for the empirical Haezendonck–Goovaerts risk measure," Insurance: Mathematics and Economics, Elsevier, vol. 55(C), pages 78-90.
    17. Tudor Răzvan & Badea Dumitru, 2017. "Operational risk quantification and modelling within Romanian insurance industry," Proceedings of the International Conference on Business Excellence, Sciendo, vol. 11(1), pages 637-648, July.
    18. Mariano González-Sánchez & Eva M. Ibáñez Jiménez & Ana I. Segovia San Juan, 2022. "Market and model risks: a feasible joint estimate methodology," Risk Management, Palgrave Macmillan, vol. 24(3), pages 187-213, September.
    19. Teresa Czerwiñska, 2013. "Ryzyko zakladow ubezpieczen w swietle nowych wymogów kapitalowych Solvency II. (Risks of insurance companies in the light of solvency capital requirement Solvency II .)," Problemy Zarzadzania, University of Warsaw, Faculty of Management, vol. 11(42), pages 8-30.
    20. Zangeneh, Pouya & McCabe, Brenda, 2022. "Modelling socio-technical risks of industrial megaprojects using Bayesian Networks and reference classes," Resources Policy, Elsevier, vol. 79(C).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bla:jrinsu:v:73:y:2006:i:2:p:193-229. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/ariaaea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.