Stress Testing at a Community Bank

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Client: In the first quarter of 2019, Abelian Partners collaborated with the executive leadership of a southeastern community bank to prepare a stress test of the bank’s overall loan book. The test was performed under guidance from OCC Bulletin 2012-33 “Community Bank Stress Testing: Supervisory Guidance,” which provides specific advice on the potential structure for stress tests at banks less than $10 billion in assets.

Engagement Scope: Using the bank’s FYE 2018 balance sheet, Abelian performed the stress test using the “Adverse” and “Severe Adverse” scenarios released from the large bank 2019 Comprehensive Capital Analysis and Review (CCAR) exercise. The stress test included 2 pieces: a “top-down” stress on the overall book and the “bottom-up” stress for loans in a particular industry concentration. The “top-down” test projected losses on a portfolio basis (segmented by call-report category), whereas the “bottom-up” test projected losses at the loan level. Overall forecast loan losses were then combined with projected losses on other balance sheet categories (securities, BOLI, REO) and with projected Pre-Provision Net Revenue (“PPNR”) under each scenario to forecast capital levels.

Loan Stress Methodology: Abelian has developed a statistical model based on macroeconomic variables versus historical bank charge-off data. For the top-down test, this statistical model was applied to the bank’s base case capital plan, which projects losses by call report category based on 3-year historical average rates, to forecast losses under each the Adverse and Severe Adverse scenario. For the bottom-up stress on the loans within the industry concentration, Abelian assigned a Probability of Default (“PD”) to each risk rating category in the loan book as well as a Loss Given Default (“LGD”) to each collateral category in the loan book. With this mapping, base case expected losses were projected at the loan level and then stressed under the Adverse and Severe Adverse scenarios using the same statistical model already developed.

Results: The stress test showed viable capital levels under both stress scenarios, suggesting that the bank was sufficiently capitalized to withstand such dramatic industry downturns. Additionally, the test showed significant losses were likely in the industry concentration when tested at the loan level, supporting the bank’s enhanced risk management strategy for this segment of the loan book. Finally, the test revealed an additional industry that could show significant losses from market volatility under stress, suggesting the need for further enhanced risk management and portfolio management activities going forward.

Takeaways: Sophisticated analytics are no longer reserved for only larger banks and financial institutions. While no amount of modeling replaces key bankers and client relationships, analytic exercises such as stress testing can benefit smaller banking institutions. When used to put a framework around a portfolio or to highlight a specific risk issue, analytics such as stress testing can enable a community bank to further its mission of serving clients in a safe and sound manner.

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