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Scenario Analysis in the Measurement of Operational Risk Capital: A Change of Measure Approach

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  • Kabir K. Dutta
  • David F. Babbel

Abstract

type="main" xml:lang="en"> At large financial institutions, operational risk is gaining the same importance as market and credit risk in the capital calculation. Although scenario analysis is an important tool for financial risk measurement, its use in the measurement of operational risk capital has been arbitrary and often inaccurate. We propose a method that combines scenario analysis with historical loss data. Using the Change of Measure approach, we evaluate the impact of each scenario on the total estimate of operational risk capital. The method can be used in stress-testing, what-if assessment for scenario analysis, and Loss Given Default estimates used in credit evaluations.

Suggested Citation

  • 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.
    • Handle: RePEc:bla:jrinsu:v:81:y:2014:i:2:p:303-334
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    References listed on IDEAS

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    1. Jackwerth, Jens Carsten & Rubinstein, Mark, 1996. "Recovering Probability Distributions from Option Prices," Journal of Finance, American Finance Association, vol. 51(5), pages 1611-1632, December.
    2. MacMinn Richard D., 2005. "On Corporate Risk Management and Insurance," Asia-Pacific Journal of Risk and Insurance, De Gruyter, vol. 1(1), pages 1-24, June.
    3. Richard D. MacMinn, 2005. "Corporate Risk Management," World Scientific Book Chapters, in: The Fisher Model And Financial Markets, chapter 9, pages 84-99, World Scientific Publishing Co. Pte. Ltd..
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    Cited by:

    1. Michal Vyskoèil, 2020. "Scenario Analysis Approach for Operational Risk in Insurance Companies," ACTA VSFS, University of Finance and Administration, vol. 14(2), pages 153-165.
    2. Martin Eling & Ruo Jia, 2017. "Recent Research Developments Affecting Nonlife Insurance—The CAS Risk Premium Project 2014 Update," Risk Management and Insurance Review, American Risk and Insurance Association, vol. 20(1), pages 63-77, March.
    3. 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.
    4. 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.
    5. Zewei Liu & Ji-Kang Chen, 2024. "Financial Resilience in China: Conceptual Framework, Risk and Protective Factors, and Empirical Evidence," Journal of Family and Economic Issues, Springer, vol. 45(4), pages 852-875, December.
    6. Christian Biener & Martin Eling & Shailee Pradhan, 2015. "Recent Research Developments Affecting Nonlife Insurance—The CAS Risk Premium Project 2013 Update," Risk Management and Insurance Review, American Risk and Insurance Association, vol. 18(1), pages 129-141, March.
    7. Georges Dionne & Samir Saissi Hassani, . "Hidden Markov regimes in operational loss data: application to the recent financial crisis," Journal of Operational Risk, Journal of Operational Risk.
    8. Olajide Solomon Fadun & Diekolola Oye, 2020. "Impacts of Operational Risk Management on Financial Performance:A Case of Commercial Banks in Nigeria," International Journal of Finance & Banking Studies, Center for the Strategic Studies in Business and Finance, vol. 9(1), pages 22-35, January.
    9. Khan, Umair & Khalid, Umair & Farooq, Fatima, 2021. "Endogeneity Quagmire Empirical Evidence from Telecommunication Industry of Pakistan," Journal of Accounting and Finance in Emerging Economies, CSRC Publishing, Center for Sustainability Research and Consultancy Pakistan, vol. 7(4), pages 955-967, December.
    10. 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.
    11. Georges Dionne & Amir Saissi Hassani, 2015. "Endogenous Hidden Markov Regimes in Operational Loss Data: Application to the Recent Financial Crisis," Cahiers de recherche 1516, CIRPEE.
    12. 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.

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    More about this item

    JEL classification:

    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G20 - Financial Economics - - Financial Institutions and Services - - - General
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages

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