IDEAS home Printed from https://ideas.repec.org/a/gam/jrisks/v8y2020i2p47-d358061.html
   My bibliography  Save this article

Implementing the Rearrangement Algorithm: An Example from Computational Risk Management

Author

Listed:
  • Marius Hofert

    (Department of Statistics and Actuarial Science, University of Waterloo, 200 University Avenue West, Waterloo, ON N2L 3G1, Canada)

Abstract

After a brief overview of aspects of computational risk management, the implementation of the rearrangement algorithm in R is considered as an example from computational risk management practice. This algorithm is used to compute the largest quantile (worst value-at-risk) of the sum of the components of a random vector with specified marginal distributions. It is demonstrated how a basic implementation of the rearrangement algorithm can gradually be improved to provide a fast and reliable computational solution to the problem of computing worst value-at-risk. Besides a running example, an example based on real-life data is considered. Bootstrap confidence intervals for the worst value-at-risk as well as a basic worst value-at-risk allocation principle are introduced. The paper concludes with selected lessons learned from this experience.

Suggested Citation

  • Marius Hofert, 2020. "Implementing the Rearrangement Algorithm: An Example from Computational Risk Management," Risks, MDPI, vol. 8(2), pages 1-28, May.
    • Handle: RePEc:gam:jrisks:v:8:y:2020:i:2:p:47-:d:358061
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-9091/8/2/47/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-9091/8/2/47/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Carole Bernard & Michel Denuit & Steven Vanduffel, 2018. "Measuring Portfolio Risk Under Partial Dependence Information," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 85(3), pages 843-863, September.
    2. 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.
    3. Embrechts, Paul & Puccetti, Giovanni & Rüschendorf, Ludger, 2013. "Model uncertainty and VaR aggregation," Journal of Banking & Finance, Elsevier, vol. 37(8), pages 2750-2764.
    4. Hofert Marius & Memartoluie Amir & Saunders David & Wirjanto Tony, 2017. "Improved algorithms for computing worst Value-at-Risk," Statistics & Risk Modeling, De Gruyter, vol. 34(1-2), pages 13-31, June.
    5. A. Ford Ramsey & Barry K. Goodwin, 2019. "Value-at-Risk and Models of Dependence in the U.S. Federal Crop Insurance Program," JRFM, MDPI, vol. 12(2), pages 1-21, April.
    6. Carole Bernard & Ludger Rüschendorf & Steven Vanduffel & Jing Yao, 2017. "How robust is the value-at-risk of credit risk portfolios?," The European Journal of Finance, Taylor & Francis Journals, vol. 23(6), pages 507-534, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Krystian Szczęsny & Stanisław Wanat & Anna Denkowska, 2023. "Solvency II and diversification effect for non-life premium and reserves risk: new results based on non-parametric copulas," Risk Management, Palgrave Macmillan, vol. 25(3), pages 1-26, September.

    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. Jonathan Ansari & Eva Lutkebohmert, 2024. "Robust Bernoulli mixture models for credit portfolio risk," Papers 2411.11522, arXiv.org.
    2. Hofert Marius & Memartoluie Amir & Saunders David & Wirjanto Tony, 2017. "Improved algorithms for computing worst Value-at-Risk," Statistics & Risk Modeling, De Gruyter, vol. 34(1-2), pages 13-31, June.
    3. Bernard, Carole & Kazzi, Rodrigue & Vanduffel, Steven, 2020. "Range Value-at-Risk bounds for unimodal distributions under partial information," Insurance: Mathematics and Economics, Elsevier, vol. 94(C), pages 9-24.
    4. Marius Hofert, 2024. "A Basic Asymptotic Test for Value-at-Risk Subadditivity," Risks, MDPI, vol. 12(12), pages 1-12, December.
    5. Pfeifer Dietmar & Mändle Andreas & Ragulina Olena, 2017. "New copulas based on general partitions-of-unity and their applications to risk management (part II)," Dependence Modeling, De Gruyter, vol. 5(1), pages 246-255, October.
    6. Yuanying Guan & Zhanyi Jiao & Ruodu Wang, 2022. "A reverse ES (CVaR) optimization formula," Papers 2203.02599, arXiv.org, revised May 2023.
    7. Claußen, Arndt & Rösch, Daniel & Schmelzle, Martin, 2019. "Hedging parameter risk," Journal of Banking & Finance, Elsevier, vol. 100(C), pages 111-121.
    8. Stephan Eckstein & Michael Kupper, 2018. "Computation of optimal transport and related hedging problems via penalization and neural networks," Papers 1802.08539, arXiv.org, revised Jan 2019.
    9. Tuitman, Jan & Vanduffel, Steven & Yao, Jing, 2020. "Correlation matrices with average constraints," Statistics & Probability Letters, Elsevier, vol. 165(C).
    10. Asimit, Alexandru V. & Gerrard, Russell, 2016. "On the worst and least possible asymptotic dependence," Journal of Multivariate Analysis, Elsevier, vol. 144(C), pages 218-234.
    11. Farkas, Walter & Fringuellotti, Fulvia & Tunaru, Radu, 2020. "A cost-benefit analysis of capital requirements adjusted for model risk," Journal of Corporate Finance, Elsevier, vol. 65(C).
    12. Jose Blanchet & Henry Lam & Yang Liu & Ruodu Wang, 2020. "Convolution Bounds on Quantile Aggregation," Papers 2007.09320, arXiv.org, revised Sep 2024.
    13. Dietmar Pfeifer & Olena Ragulina, 2020. "Generating unfavourable VaR scenarios with patchwork copulas," Papers 2011.06281, arXiv.org, revised May 2021.
    14. Ruodu Wang & Ricardas Zitikis, 2018. "Weak comonotonicity," Papers 1812.04827, arXiv.org, revised Sep 2019.
    15. Koch-Medina, Pablo & Munari, Cosimo & Svindland, Gregor, 2018. "Which eligible assets are compatible with comonotonic capital requirements?," Insurance: Mathematics and Economics, Elsevier, vol. 81(C), pages 18-26.
    16. Yuyu Chen & Peng Liu & Yang Liu & Ruodu Wang, 2022. "Ordering and inequalities for mixtures on risk aggregation," Mathematical Finance, Wiley Blackwell, vol. 32(1), pages 421-451, January.
    17. Takaaki Koike & Liyuan Lin & Ruodu Wang, 2022. "Joint mixability and notions of negative dependence," Papers 2204.11438, arXiv.org, revised Jan 2024.
    18. Carole Bernard & Silvana M. Pesenti & Steven Vanduffel, 2024. "Robust distortion risk measures," Mathematical Finance, Wiley Blackwell, vol. 34(3), pages 774-818, July.
    19. Cornilly, Dries & Vanduffel, Steven, 2019. "Equivalent distortion risk measures on moment spaces," Statistics & Probability Letters, Elsevier, vol. 146(C), pages 187-192.
    20. Lux, Thibaut & Papapantoleon, Antonis, 2019. "Model-free bounds on Value-at-Risk using extreme value information and statistical distances," Insurance: Mathematics and Economics, Elsevier, vol. 86(C), pages 73-83.

    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:gam:jrisks:v:8:y:2020:i:2:p:47-:d:358061. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.