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Operational Risk and Risk Management Quality: Evidence from U.S. Bank Holding Companies

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Abstract

This study documents the association between the quality of risk management practices and operational losses at large U.S. financial institutions. Using detailed supervisory data, we find that companies with weak risk management practices experience higher and more volatile operational losses. We also present evidence that the strength of risk management practices prior to the 2008–2009 Financial Crisis has explanatory power over losses during the crisis period. Our analysis provides new evidence of the importance of risk management practices for curtailing risk at financial institutions.

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Notes

  1. The International Monetary Fund’s Managing Director, Christine Lagarde, stated: “A key failure during the crisis was in the internal control and risk management systems of institutions. Think of the recent example of the “London Whale.” In many cases, financial risks were either ignored or underestimated – and in the particular case of systemic risks, they were not well understood. Failure happened at both the management and board levels.” (“Ethics and Finance – Aligning Financial Incentives with Societal Objectives”, Washington D.C., May 6, 2015).

  2. Senior Supervisors Group (SSG) is a group of supervisory agencies from France, Germany, Switzerland, the United Kingdom, and the United States.

  3. See Financial Times: “UBS trader Adoboli held over $2bn loss” (M. Murphy, K. Burgess, S. Jones, and H. Simonian, September 15, 2011).

  4. See Reuters: “Deutsche Bank fined record $2.5 billion over rate rigging” (K. Ridley and K. Freifeld, April 23, 2015).

  5. The SR 04-18 document of the Federal Reserve System provides the detailed description of the BHC rating system and is available at http://www.federalreserve.gov/boarddocs/srletters/2004/sr0418.htm.

  6. The volatility of operational losses is measured as the standard deviation of realized operational losses as a proportion of BHC assets using a forward-looking four-quarter rolling window.

  7. Despite the availability of financial models to measure risk exposures, it is important to recognize that financial models are only as good as their underlying assumptions. Many have recognized the role of incorrect financial model assumptions in fuelling the recent financial crisis.

  8. Theoretical work in this area include among others: Froot et al. (1993), Froot and Stein (1998), Fehle and Tsyplakov (2005), Purnanandam (2008), Rampini and Viswanathan (2010), Bolton et al. (2011), Ergashev (2012), Gamba and Triantis (2014), Rampini et al. (2014), Abdymomunov et al. (2015a).

  9. More broadly, our research also contributes to the large body of research on bank risk and performance (e.g., Keeley 1990; Demsetz and Strahan 1997; Hellmann et al. 2000; Demirguc-Kunt and Detragiache 2002; Laeven and Levine 2009).

  10. The FR Y-14Q reporting form and the filing instructions are publicly available online at http://www.federalreserve.gov/apps/reportforms/.

  11. Some of these cross-sectional variations also result from differences in the interpretation of reporting instructions by BHCs as well as uncertainties about when loss events actually occurred or were discovered. For example, some of the biggest operational losses in our data set are related to misrepresentations to the public about securities containing toxic mortgages. Consider a bank with poor underwriting standards that packaged, marketed, sold and issued residential mortgage-backed securities (RMBS) over extended periods of time prior to the 2008–2009 crisis, and subsequently paid penalties to resolve federal and state civil claims about its practices post crisis. Assigning loss occurrence and discovery dates in such instances are challenging and practices substantially differ across institutions.

  12. We note that, according to accounting standards, BHCs have to reserve for probable and estimable losses. Most significant operational losses (in dollar terms) are reported in our data set using accounting dates reflecting when BHCs first start reserving for potential losses. Such dates are significantly earlier than the dates when losses settle, and become known to outsiders.

  13. We also verify the robustness of our results to scaling operational losses by other variables such as BHC total liabilities and book value of equity. See Section 3.5 for more details.

  14. The RFI/C(D) system has only been in place since 2005. Thus, we supplement RFI/C(D) data with information from the BOPEC rating system for BHC-quarters prior to 2005. The BOPEC BHC rating system included an analogous composite risk management rating pre-2005. For more information on the RFI/C(D) and BOPEC rating systems see the following supervisory letters: SL 95–91, SL 95–69, SR 95–17, SR 95–22, and SR 04–18.

  15. More information on U.S. business cycle expansions and contractions can be found at: http://www.nber.org/cycles.html.

  16. See Abdymomunov and Curti (2015) for a recent discussion of operational loss distribution properties and implications for operational risk modeling.

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Authors and Affiliations

  1. Quantitative Supervision & Research Group, Federal Reserve Bank of Richmond, Richmond, VA, USA

    Azamat Abdymomunov & Atanas Mihov

  2. 530 E. Trade St, Charlotte, NC, 28202, USA

    Azamat Abdymomunov & Atanas Mihov

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  1. Azamat Abdymomunov
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  2. Atanas Mihov
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Correspondence to Atanas Mihov.

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Abdymomunov, A., Mihov, A. Operational Risk and Risk Management Quality: Evidence from U.S. Bank Holding Companies. J Financ Serv Res 56, 73–93 (2019). https://doi.org/10.1007/s10693-017-0284-3

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  • DOI: https://doi.org/10.1007/s10693-017-0284-3

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