IDEAS home Printed from https://ideas.repec.org/p/hal/cesptp/halshs-00771387.html
   My bibliography  Save this paper

Using a time series approach to correct serial correlation in operational risk capital calculation

Author

Listed:
  • Dominique Guegan

    (CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique)

  • Bertrand Hassani

    (CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique)

Abstract

The advanced measurement approach requires financial institutions to develop internal models to evaluate regulatory capital. Traditionally, the loss distribution approach (LDA) is used, mixing frequencies and severities to build a loss distribution function (LDF). This distribution represents annual losses; consequently, the 99.9th percentile of the distribution providing the capital charge denotes the worst year in a thousand. The traditional approach approved by the regulator and implemented by financial institutions assumes the losses are independent. This paper proposes a solution to address the issues arising when autocorrelations are detected between the losses, by using time series. Thus, the losses are aggregated periodically and several models are adjusted using autoregressive models, autoregressive fractionally integrated and Gegenbauer processes considering various distributions fitted on the residuals. Monte Carlo simulation enables the construction of the LDF, and the computation of the relevant risk measures. These dynamic approaches are compared with static traditional methodologies in order to show their impact on the capital charges, using several data sets. The construction of the related LDFs and the computation of the capital charges permit complying with the regulation. Besides, capturing simultaneously autocorrelation phenomena and large losses by fitting adequate distributions on the residuals, provide an alternative to the arbitrary selection of the LDA.

Suggested Citation

  • Dominique Guegan & Bertrand Hassani, 2013. "Using a time series approach to correct serial correlation in operational risk capital calculation," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00771387, HAL.
  • Handle: RePEc:hal:cesptp:halshs-00771387
    Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-00771387v2
    as

    Download full text from publisher

    File URL: https://shs.hal.science/halshs-00771387v2/document
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    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. Jarque, Carlos M. & Bera, Anil K., 1980. "Efficient tests for normality, homoscedasticity and serial independence of regression residuals," Economics Letters, Elsevier, vol. 6(3), pages 255-259.
    3. repec:hal:wpaper:halshs-00722029 is not listed on IDEAS
    4. Allen, Linda & Bali, Turan G., 2007. "Cyclicality in catastrophic and operational risk measurements," Journal of Banking & Finance, Elsevier, vol. 31(4), pages 1191-1235, April.
    5. Dominique Guegan & Bertrand Hassani & Cédric Naud, 2011. "An efficient threshold choice for operational risk capital computation," Post-Print halshs-00790217, HAL.
    6. Bertrand Hassani & Alexis Renaudin, 2013. "The Cascade Bayesian Approach for a controlled integration of internal data, external data and scenarios," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00795046, HAL.
    7. Bertrand Hassani & Alexis Renaudin, 2013. "The Cascade Bayesian Approach for a controlled integration of internal data, external data and scenarios," Post-Print halshs-00795046, HAL.
    8. repec:cup:jfinqa:v:46:y:2011:i:06:p:1683-1725_00 is not listed on IDEAS
    9. repec:mse:cesdoc:13009 is not listed on IDEAS
    10. Dominique Guegan & Bertrand Hassani, 2011. "Operational risk: A Basel II++ step before Basel III," Post-Print halshs-00639484, HAL.
    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. 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.
    2. Bertrand K. Hassani, 2014. "Risk Appetite in Practice: Vulgaris Mathematica," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-01020293, HAL.
    3. Bertrand K Hassani, 2014. "Risk Appetite in Practice: Vulgaris Mathematica," Documents de travail du Centre d'Economie de la Sorbonne 14037, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    4. Dominique Guegan & Bertrand K Hassani, 2014. "Stress Testing Engineering: the real risk measurement?," Documents de travail du Centre d'Economie de la Sorbonne 14006, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    5. Dominique Guegan & Bertrand Hassani, 2014. "Stress Testing Engineering: the real risk measurement?," Post-Print halshs-00951593, HAL.
    6. Dominique Guegan & Bertrand Hassani, 2012. "Multivariate VaRs for Operational Risk Capital Computation: a Vine Structure Approach," Post-Print halshs-00587706, HAL.
    7. Gillet, Roland & Hübner, Georges & Plunus, Séverine, 2010. "Operational risk and reputation in the financial industry," Journal of Banking & Finance, Elsevier, vol. 34(1), pages 224-235, January.
    8. Roc'io Paredes & Marco Vega, 2020. "An internal fraud model for operational losses in retail banking," Papers 2002.03235, arXiv.org.
    9. Xu, Chi & Zheng, Chunling & Wang, Donghua & Ji, Jingru & Wang, Nuan, 2019. "Double correlation model for operational risk: Evidence from Chinese commercial banks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 516(C), pages 327-339.
    10. Dionne, Georges & Saissi-Hassani, Samir, 2016. "Hidden Markov Regimes in Operational Loss Data: Application to the Recent Financial Crisis," Working Papers 15-3, HEC Montreal, Canada Research Chair in Risk Management.
    11. Dominique Guegan & Bertrand Hassani, 2011. "Multivariate VaRs for Operational Risk Capital Computation: a Vine Structure Approach," Documents de travail du Centre d'Economie de la Sorbonne 11017rr, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne, revised Apr 2012.
    12. Dahen, Hela & Dionne, Georges, 2010. "Scaling models for the severity and frequency of external operational loss data," Journal of Banking & Finance, Elsevier, vol. 34(7), pages 1484-1496, July.
    13. Bertrand K. Hassani, 2014. "Risk Appetite in Practice: Vulgaris Mathematica," Post-Print halshs-01020293, HAL.
    14. Iñaki Aldasoro & Leonardo Gambacorta & Paolo Giudici & Thomas Leach, 2023. "Operational and Cyber Risks in the Financial Sector," International Journal of Central Banking, International Journal of Central Banking, vol. 19(5), pages 340-402, December.
    15. Dominique Guégan & Wayne Tarrant, 2012. "On the necessity of five risk measures," Annals of Finance, Springer, vol. 8(4), pages 533-552, November.
    16. Juncal Cunado & David Gabauer & Rangan Gupta, 2024. "Realized volatility spillovers between energy and metal markets: a time-varying connectedness approach," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-17, December.
    17. Elie Bouri & Georges Azzi, 2014. "On the Dynamic Transmission of Mean and Volatility across the Arab Stock Markets," Journal of Emerging Market Finance, Institute for Financial Management and Research, vol. 13(3), pages 279-304, December.
    18. Eric Fur, 2023. "Risk and return of classic car market prices: passion or financial investment?," Journal of Asset Management, Palgrave Macmillan, vol. 24(1), pages 59-68, February.
    19. Georgiev, Iliyan, 2010. "Model-based asymptotic inference on the effect of infrequent large shocks on cointegrated variables," Journal of Econometrics, Elsevier, vol. 158(1), pages 37-50, September.
    20. Jin, Xiaoye, 2015. "Volatility transmission and volatility impulse response functions among the Greater China stock markets," Journal of Asian Economics, Elsevier, vol. 39(C), pages 43-58.

    More about this item

    Keywords

    Operational risk; time series; Gegenbauer processes; Monte Carlo; risk measures; Risque opérationnel; séries chronologiques; Gegenbauer processus; mesure du risque;
    All these keywords.

    JEL classification:

    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:hal:cesptp:halshs-00771387. 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: CCSD (email available below). General contact details of provider: https://hal.archives-ouvertes.fr/ .

    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.