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Predicting Operational Loss Exposure Using Past Losses

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Abstract

Operational risk models, such as the loss distribution approach, frequently use past internal losses to forecast operational loss exposure. However, the ability of past losses to predict exposure, particularly tail exposure, has not been thoroughly examined in the literature. In this paper, we test whether simple metrics derived from past loss experience are predictive of future tail operational loss exposure using quantile regression. We find evidence that past losses are predictive of future exposure, particularly metrics related to loss frequency.

Suggested Citation

  • Filippo Curti & Marco Migueis, 2016. "Predicting Operational Loss Exposure Using Past Losses," Finance and Economics Discussion Series 2016-2, Board of Governors of the Federal Reserve System (U.S.).
    • Handle: RePEc:fip:fedgfe:2016-02
    DOI: 10.17016/FEDS.2016.002r1
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    1. Gaglianone, Wagner Piazza & Lima, Luiz Renato & Linton, Oliver & Smith, Daniel R., 2011. "Evaluating Value-at-Risk Models via Quantile Regression," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(1), pages 150-160.
    2. Pérignon, Christophe & Deng, Zi Yin & Wang, Zhi Jun, 2008. "Do banks overstate their Value-at-Risk?," Journal of Banking & Finance, Elsevier, vol. 32(5), pages 783-794, May.
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    4. Cope, Eric W. & Piche, Mark T. & Walter, John S., 2012. "Macroenvironmental determinants of operational loss severity," Journal of Banking & Finance, Elsevier, vol. 36(5), pages 1362-1380.
    5. Tae-Hwy Lee & Yong Bao & Burak Saltoglu, 2006. "Evaluating predictive performance of value-at-risk models in emerging markets: a reality check," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(2), pages 101-128.
    6. Agarwal, Vineet & Taffler, Richard, 2008. "Comparing the performance of market-based and accounting-based bankruptcy prediction models," Journal of Banking & Finance, Elsevier, vol. 32(8), pages 1541-1551, August.
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    8. Azamat Abdymomunov & Filippo Curti & Atanas Mihov, 2020. "U.S. Banking Sector Operational Losses and the Macroeconomic Environment," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 52(1), pages 115-144, February.
    9. Jeremy Berkowitz & James O'Brien, 2002. "How Accurate Are Value‐at‐Risk Models at Commercial Banks?," Journal of Finance, American Finance Association, vol. 57(3), pages 1093-1111, June.
    10. Edward I. Altman & Gabriele Sabato, 2013. "MODELING CREDIT RISK FOR SMEs: EVIDENCE FROM THE US MARKET," World Scientific Book Chapters, in: Oliviero Roggi & Edward I Altman (ed.), Managing and Measuring Risk Emerging Global Standards and Regulations After the Financial Crisis, chapter 9, pages 251-279, World Scientific Publishing Co. Pte. Ltd..
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    Cited by:

    1. Filippo Curti & Marco Migueis & Rob T. Stewart, 2019. "Benchmarking Operational Risk Stress Testing Models," Finance and Economics Discussion Series 2019-038, Board of Governors of the Federal Reserve System (U.S.).
    2. Marco Migueis, 2019. "Evaluating the AMA and the new standardized approach for operational risk capital," Journal of Banking Regulation, Palgrave Macmillan, vol. 20(4), pages 302-311, December.

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    More about this item

    Keywords

    Banking Regulation; Risk Management; Operational Risk; Tail Risk; Quantile Regression;
    All these keywords.

    JEL classification:

    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

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