IDEAS home Printed from https://ideas.repec.org/a/wly/isacfm/v20y2013i3p163-187.html
   My bibliography  Save this article

Scenario Generation For Operational Risk

Author

Listed:
  • Sovan Mitra

Abstract

Operational risk is an increasingly important area of risk management. Scenarios are an important modelling tool in operational risk management as alternative viable methods may not exist. This can be due to challenging modelling, data and implementation issues, and other methods fail to take into account expert information. The use of scenarios has been recommended by regulators; however, scenarios can be unreliable, unrealistic and fail to take into account quantitative data. These problems have also been identified by regulators such as Basel, and presently little literature exists on addressing the problem of generating scenarios for operational risk. In this paper we propose a method for generating operational risk scenarios. We employ the method of cluster analysis to generate scenarios that enable one to combine expert opinion scenarios with quantitative operational risk data. We show that this scenario generation method leads to significantly improved scenarios and significant advantages for operational risk applications. In particular for operational risk modelling, our method leads to resolving the key problem of combining two sources of information without eliminating the information content gained from expert opinions, tractable computational implementation for operational risk modelling, improved stress testing, what‐if analyses and the ability to apply our method to a wide range of quantitative operational risk data (including multivariate distributions). We conduct numerical experiments on our method to demonstrate and validate its performance and compare it against scenarios generated from statistical property matching for comparison. Copyright © 2013 John Wiley & Sons, Ltd.

Suggested Citation

  • Sovan Mitra, 2013. "Scenario Generation For Operational Risk," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 20(3), pages 163-187, July.
    • Handle: RePEc:wly:isacfm:v:20:y:2013:i:3:p:163-187
    DOI: 10.1002/isaf.1341
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/isaf.1341
    Download Restriction: no
    File URL: https://libkey.io/10.1002/isaf.1341?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. Gareth W. Peters & Pavel Shevchenko & Mark Young & Wendy Yip, 2011. "Analytic Loss Distributional Approach Model for Operational Risk from the alpha-Stable Doubly Stochastic Compound Processes and Implications for Capital Allocation," Papers 1102.3582, arXiv.org.
    2. Philip M. Lurie & Matthew S. Goldberg, 1998. "An Approximate Method for Sampling Correlated Random Variables from Partially-Specified Distributions," Management Science, INFORMS, vol. 44(2), pages 203-218, February.
    3. 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.
    4. James E. Smith, 1993. "Moment Methods for Decision Analysis," Management Science, INFORMS, vol. 39(3), pages 340-358, March.
    5. Shankar Basu & Roger G. Schroeder, 1977. "Incorporating Judgments in Sales Forecasts: Application of the Delphi Method at American Hoist & Derrick," Interfaces, INFORMS, vol. 7(3), pages 18-27, May.
    6. Kabir K. Dutta & David F. Babbel, 2014. "Scenario Analysis in the Measurement of Operational Risk Capital: A Change of Measure Approach," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 81(2), pages 303-334, June.
    7. Gulpinar, Nalan & Rustem, Berc & Settergren, Reuben, 2004. "Simulation and optimization approaches to scenario tree generation," Journal of Economic Dynamics and Control, Elsevier, vol. 28(7), pages 1291-1315, April.
    8. Babbel, David F., 2010. "A Note on Scenario Analysis in the Measurement of Operational Risk Capital: A Change of Measure Approach," Working Papers 10-26, University of Pennsylvania, Wharton School, Weiss Center.
    9. Loader, David, 2002. "Controls, Procedures and Risk," Elsevier Monographs, Elsevier, edition 1, number 9780750654869.
    10. Bakhodir Ergashev, 2012. "A Theoretical Framework for Incorporating Scenarios into Operational Risk Modeling," Journal of Financial Services Research, Springer;Western Finance Association, vol. 41(3), pages 145-161, June.
    11. 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.
    12. Gareth W. Peters & Pavel V. Shevchenko & Mario V. Wuthrich, 2009. "Dynamic operational risk: modeling dependence and combining different sources of information," Papers 0904.4074, arXiv.org, revised Jul 2009.
    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. Gareth W. Peters & Rodrigo S. Targino & Pavel V. Shevchenko, 2013. "Understanding Operational Risk Capital Approximations: First and Second Orders," Papers 1303.2910, arXiv.org.
    2. Mitra, Sovan & Lim, Sungmook & Karathanasopoulos, Andreas, 2019. "Regression based scenario generation: Applications for performance management," Operations Research Perspectives, Elsevier, vol. 6(C).
    3. Ponomareva, K. & Roman, D. & Date, P., 2015. "An algorithm for moment-matching scenario generation with application to financial portfolio optimisation," European Journal of Operational Research, Elsevier, vol. 240(3), pages 678-687.
    4. Babbel, David F., 2010. "A Note on Scenario Analysis in the Measurement of Operational Risk Capital: A Change of Measure Approach," Working Papers 10-26, University of Pennsylvania, Wharton School, Weiss Center.
    5. Wu, Dexiang & Wu, Desheng Dash, 2020. "A decision support approach for two-stage multi-objective index tracking using improved lagrangian decomposition," Omega, Elsevier, vol. 91(C).
    6. Patrizia Beraldi & Maria Bruni, 2014. "A clustering approach for scenario tree reduction: an application to a stochastic programming portfolio optimization problem," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 22(3), pages 934-949, October.
    7. Davi Valladão & Thuener Silva & Marcus Poggi, 2019. "Time-consistent risk-constrained dynamic portfolio optimization with transactional costs and time-dependent returns," Annals of Operations Research, Springer, vol. 282(1), pages 379-405, November.
    8. Azamat Abdymomunov & Sharon Blei & Bakhodir Ergashev, 2015. "Integrating Stress Scenarios into Risk Quantification Models," Journal of Financial Services Research, Springer;Western Finance Association, vol. 47(1), pages 57-79, February.
    9. Targino, Rodrigo S. & Peters, Gareth W. & Shevchenko, Pavel V., 2015. "Sequential Monte Carlo Samplers for capital allocation under copula-dependent risk models," Insurance: Mathematics and Economics, Elsevier, vol. 61(C), pages 206-226.
    10. Pavel V. Shevchenko, 2009. "Implementing Loss Distribution Approach for Operational Risk," Papers 0904.1805, arXiv.org, revised Jul 2009.
    11. Bakhodir Ergashev, 2012. "A Theoretical Framework for Incorporating Scenarios into Operational Risk Modeling," Journal of Financial Services Research, Springer;Western Finance Association, vol. 41(3), pages 145-161, June.
    12. Konstantin Pavlikov & Stan Uryasev, 2018. "CVaR distance between univariate probability distributions and approximation problems," Annals of Operations Research, Springer, vol. 262(1), pages 67-88, March.
    13. Moghaddam, Iman Gerami & Nick, Mostafa & Fallahi, Farhad & Sanei, Mohsen & Mortazavi, Saeid, 2013. "Risk-averse profit-based optimal operation strategy of a combined wind farm–cascade hydro system in an electricity market," Renewable Energy, Elsevier, vol. 55(C), pages 252-259.
    14. Pavel V. Shevchenko, 2010. "Implementing loss distribution approach for operational risk," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 26(3), pages 277-307, May.
    15. Soumyadip Ghosh & Henry Lam, 2019. "Robust Analysis in Stochastic Simulation: Computation and Performance Guarantees," Operations Research, INFORMS, vol. 67(1), pages 232-249, January.
    16. Ignatieva, Katja & Landsman, Zinoviy, 2019. "Conditional tail risk measures for the skewed generalised hyperbolic family," Insurance: Mathematics and Economics, Elsevier, vol. 86(C), pages 98-114.
    17. M. Schyns & Y. Crama & G. Hübner, 2010. "Optimal selection of a portfolio of options under Value-at-Risk constraints: a scenario approach," Annals of Operations Research, Springer, vol. 181(1), pages 683-708, December.
    18. Alice X. D. Dong & Jennifer S. K. Chan & Gareth W. Peters, 2014. "Risk Margin Quantile Function Via Parametric and Non-Parametric Bayesian Quantile Regression," Papers 1402.2492, arXiv.org.
    19. Kylie-Anne Richards & Gareth W. Peters & William Dunsmuir, 2012. "Heavy-Tailed Features and Empirical Analysis of the Limit Order Book Volume Profiles in Futures Markets," Papers 1210.7215, arXiv.org, revised Apr 2015.
    20. Gareth W. Peters & Wilson Ye Chen & Richard H. Gerlach, 2016. "Estimating Quantile Families of Loss Distributions for Non-Life Insurance Modelling via L-Moments," Risks, MDPI, vol. 4(2), pages 1-41, May.

    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:wly:isacfm:v:20:y:2013:i:3:p:163-187. 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: http://www.interscience.wiley.com/jpages/1099-1174/ .

    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.