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A test of the inherent predictiveness of the RU, a new metric to express all forms of operational risk in banks

Author

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  • Hughes, Peter

    (Risk Accounting Standards Board, Durham University Business School, UK)

  • Marzouk, Mahmoud

    (University of Leicester, UK)

Abstract

In 2016 Allan D. Grody and Peter J. Hughes proposed a method and system termed ‘Risk Accounting’, an integrated financial and risk accounting framework. Risk Accounting incorporates a novel operational risk exposure quantification technique based on the Risk Unit (RU), a new common additive metric designed to express all forms of operational risk in banks. In this paper, we report on initial tests of the inherent predictiveness of the RU. The test focused on the period leading up to the global financial crisis of 2007-8 and involved the restatement into RUs of publicly available accounting data in the United States relative to a subset of large US banks. We contend that the RU’s inherent predictiveness could be concluded if it is demonstrated that an accelerated increase in trended operational risk RUs and subsequent material unexpected losses are positively correlated. We further describe how a monetary value can be stochastically derived and assigned to the RU over time. The inclusion of valued RUs in accounting systems will potentially enable the systematic adjustment of financial performance and condition relative to accepted nonfinancial risks to complement the accounting treatment already applied to financial (credit and market) risks. The resulting harmonisation of the accounting treatment applied to both financial and nonfinancial risks based on stochastic modelling will enable risk-adjusted economic profit to be adopted as the primary business performance metric and economic capital as the primary method of determining both operating and regulatory capital requirements. The real-time or near-real-time production of portfolio views of operational risk exposures based on the RU adds analytical rigour to their management and causes risk mitigation to become both a risk reduction and a profit optimisation initiative. The more effective management, oversight and governance of exposures to operational risks is the anticipated outcome.

Suggested Citation

  • Hughes, Peter & Marzouk, Mahmoud, 2021. "A test of the inherent predictiveness of the RU, a new metric to express all forms of operational risk in banks," Journal of Risk Management in Financial Institutions, Henry Stewart Publications, vol. 14(2), pages 173-194, March.
    • Handle: RePEc:aza:rmfi00:y:2021:v:14:i:2:p:173-194
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    References listed on IDEAS

    as
    1. Grody, Allan D. & Hughes, Peter J., 2016. "Risk Accounting - Part 1: The risk data aggregation and risk reporting (BCBS 239) foundation of enterprise risk management (ERM) and risk governance," Journal of Risk Management in Financial Institutions, Henry Stewart Publications, vol. 9(2), pages 130-146, March.
    2. Ellis, Luci & Haldane, Andy & Moshirian, Fariborz, 2014. "Systemic risk, governance and global financial stability," Journal of Banking & Finance, Elsevier, vol. 45(C), pages 175-181.
    3. Jean-Charles Rochet, 2010. "Commentary: Systemic Risk: Changing the Regulatory Perspective," International Journal of Central Banking, International Journal of Central Banking, vol. 6(34), pages 259-276, December.
    4. Grody, Allan D. & Hughes, Peter J., 2016. "Risk accounting - part 2: The risk data aggregation and risk reporting (BCBS 239) foundation of enterprise risk management (ERM) and risk governance," Journal of Risk Management in Financial Institutions, Henry Stewart Publications, vol. 9(3), pages 224-248, June.
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    More about this item

    Keywords

    operational risk; risk accounting; risk quantification; expected loss; unexpected loss;
    All these keywords.

    JEL classification:

    • G2 - Financial Economics - - Financial Institutions and Services
    • E5 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit

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