IDEAS home Printed from https://ideas.repec.org/p/boj/bojron/07-e-1226.html
   My bibliography  Save this paper

The Effect of the Choice of the Loss Severity Distribution and the Parameter Estimation Method on Operational Risk Measurement - Analysis Using Sample Data -

Author

Listed:
  • Financial Systems and Bank Examination Department

    (Bank of Japan)

Abstract

A number of financial institutions in Japan and overseas employ the loss distribution approach as an operational risk measurement technique. However, as yet, there is no standard practice. There are wide variations, especially in the specifications of the models used, the assumed loss severity distribution and the parameter estimation methods. In this paper we introduce a series of processes for the measurement of operational risk: estimation of the loss severity distribution: estimation of the loss distribution and assessment of the results. For that purpose, we present an example of operational risk quantification for a sample data set that has the characteristics summarized below.

Suggested Citation

  • Financial Systems and Bank Examination Department, 2007. "The Effect of the Choice of the Loss Severity Distribution and the Parameter Estimation Method on Operational Risk Measurement - Analysis Using Sample Data -," Bank of Japan Research Papers 2007-12-26, Bank of Japan.
  • Handle: RePEc:boj:bojron:07-e-1226
    as

    Download full text from publisher

    File URL: http://www.boj.or.jp/en/research/brp/ron_2007/data/ron0712c.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kabir Dutta & Jason Perry, 2006. "A tale of tails: an empirical analysis of loss distribution models for estimating operational risk capital," Working Papers 06-13, Federal Reserve Bank of Boston.
    2. Ariane Chapelle & Yves Crama & Georges Hubner & Jean-Philippe Peeters, 2004. "Basel II and Operational Risk: Implications for risk measurement and management in the financial sector," Working Paper Research 51, National Bank of Belgium.
    3. de Fontnouvelle, Patrick & Dejesus-Rueff, Virginia & Jordan, John S. & Rosengren, Eric S., 2006. "Capital and Risk: New Evidence on Implications of Large Operational Losses," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 38(7), pages 1819-1846, October.
    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. Chernobai, Anna & Yildirim, Yildiray, 2008. "The dynamics of operational loss clustering," Journal of Banking & Finance, Elsevier, vol. 32(12), pages 2655-2666, December.
    2. Chernobai, Anna & Ozdagli, Ali & Wang, Jianlin, 2021. "Business complexity and risk management: Evidence from operational risk events in U.S. bank holding companies," Journal of Monetary Economics, Elsevier, vol. 117(C), pages 418-440.
    3. José Ruiz-Canela López, 2021. "How Can Enterprise Risk Management Help in Evaluating the Operational Risks for a Telecommunications Company?," JRFM, MDPI, vol. 14(3), pages 1-26, March.
    4. Peters, Gareth W. & Shevchenko, Pavel V. & Young, Mark & Yip, Wendy, 2011. "Analytic loss distributional approach models for operational risk from the α-stable doubly stochastic compound processes and implications for capital allocation," Insurance: Mathematics and Economics, Elsevier, vol. 49(3), pages 565-579.
    5. S�verine Plunus & Georges Hübner & Jean-Philippe Peters, 2012. "Measuring operational risk in financial institutions," Applied Financial Economics, Taylor & Francis Journals, vol. 22(18), pages 1553-1569, September.
    6. Buch-Kromann, Tine & Guillén, Montserrat & Linton, Oliver & Nielsen, Jens Perch, 2011. "Multivariate density estimation using dimension reducing information and tail flattening transformations," Insurance: Mathematics and Economics, Elsevier, vol. 48(1), pages 99-110, January.
    7. Filippo Curti & W. Scott Frame & Atanas Mihov, 2022. "Are the Largest Banking Organizations Operationally More Risky?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 54(5), pages 1223-1259, August.
    8. Mizgier, Kamil J. & Hora, Manpreet & Wagner, Stephan M. & Jüttner, Matthias P., 2015. "Managing operational disruptions through capital adequacy and process improvement," European Journal of Operational Research, Elsevier, vol. 245(1), pages 320-332.
    9. Berger, Allen N. & Curti, Filippo & Mihov, Atanas & Sedunov, John, 2022. "Operational Risk is More Systemic than You Think: Evidence from U.S. Bank Holding Companies," Journal of Banking & Finance, Elsevier, vol. 143(C).
    10. Lu Wei & Jianping Li & Xiaoqian Zhu, 2018. "Operational Loss Data Collection: A Literature Review," Annals of Data Science, Springer, vol. 5(3), pages 313-337, September.
    11. Enrique Bonsón & Tomás Escobar & Francisco Flores, 2007. "Sub‐Optimality of Income Statement‐Based Methods for Measuring Operational Risk under Basel II: Empirical Evidence from Spanish Banks," Financial Markets, Institutions & Instruments, John Wiley & Sons, vol. 16(4), pages 201-220, November.
    12. Thomas Conlon & Xing Huan & Steven Ongena, 2020. "Operational Risk Capital," Swiss Finance Institute Research Paper Series 20-55, Swiss Finance Institute.
    13. Martin Eling & Kwangmin Jung, 2022. "Heterogeneity in cyber loss severity and its impact on cyber risk measurement," Risk Management, Palgrave Macmillan, vol. 24(4), pages 273-297, December.
    14. Robert Jarrow, 2017. "Operational Risk," World Scientific Book Chapters, in: THE ECONOMIC FOUNDATIONS OF RISK MANAGEMENT Theory, Practice, and Applications, chapter 8, pages 69-70, World Scientific Publishing Co. Pte. Ltd..
    15. Valérie Chavez-Demoulin & Paul Embrechts & Marius Hofert, 2016. "An Extreme Value Approach for Modeling Operational Risk Losses Depending on Covariates," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 83(3), pages 735-776, September.
    16. M. Bee & J. Hambuckers & L. Trapin, 2019. "Estimating Value-at-Risk for the g-and-h distribution: an indirect inference approach," Quantitative Finance, Taylor & Francis Journals, vol. 19(8), pages 1255-1266, August.
    17. Suren Pakhchanyan, 2016. "Operational Risk Management in Financial Institutions: A Literature Review," IJFS, MDPI, vol. 4(4), pages 1-21, October.
    18. Hu, Mingya & Zhang, Yongjie & Feng, Xu & Xiong, Xiong, 2024. "How technological innovation influence operational risk: Evidence from banks in China," International Review of Financial Analysis, Elsevier, vol. 95(PB).
    19. Yuan Hong & Shaojian Qu, 2024. "Beyond Boundaries: The AHP-DEA Model for Holistic Cross-Banking Operational Risk Assessment," Mathematics, MDPI, vol. 12(7), pages 1-18, March.
    20. Marco Bee & Julien Hambuckers & Flavio Santi & Luca Trapin, 2021. "Testing a parameter restriction on the boundary for the g-and-h distribution: a simulated approach," Computational Statistics, Springer, vol. 36(3), pages 2177-2200, September.

    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:boj:bojron:07-e-1226. 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: Bank of Japan (email available below). General contact details of provider: https://edirc.repec.org/data/bojgvjp.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.