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

Author

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  • Dutta, Kabir K.

    (Charles River Associates International, Boston, MA)

  • Babbel, David F.

    (University of PA and Charles River Associates International, Boston, MA)

Abstract

Operational risk is now increasingly being considered an important financial risk and has been gaining importance similar to market and credit risk. In particular, in the banking regulation for large financial institutions it is required that operational risk be separately measured. The capital being held to safeguard against such risk is very significant at a large financial institution. As our understanding of such risk is evolving so are the methodologies for measuring such risk. While scenario analysis is an important tool for financial risk measurement, its use in the measurement of operational risk capital has been quite arbitrary and often inaccurate. The importance of scenario analysis cannot be overstated. The Federal Reserve System used scenarios to stress test the risk exposures of a financial institution during a recent financial crisis. We propose a method for the measurement of operational risk exposure of an institution using scenario analysis and internal loss data. Using the Change of Measure approach used for asset pricing in financial economics we evaluate the impact of each scenario in the total estimate of the operational risk capital. We show that the proposed method can be used in many situations, such as the calculation of operational risk capital, stress testing, and what-if assessment for scenario analysis. By using this method one could also generate a key ingredient that is a precursor toward creating a catastrophe bond on various segments of operational risk exposures of an institution. Although the method described here is in the context of operational loss, it can be used in modeling scenarios in many other contexts, such as insurance pricing, marketing forecast, and credit evaluations.

Suggested Citation

  • Dutta, Kabir K. & Babbel, David F., 2010. "Scenario Analysis in the Measurement of Operational Risk Capital: A Change of Measure Approach," Working Papers 10-10, University of Pennsylvania, Wharton School, Weiss Center.
    • Handle: RePEc:ecl:upafin:10-10
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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. 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..
    3. 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.
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    Citations

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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. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    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.

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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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