FRM Corner

Preventing the Next Bank Failure: Lessons Learned from the Fall of Republic First

Coinciding with the post-COVID struggles of the commercial real estate (CRE) sector in the U.S., more regional bank failures could be on the horizon. What did the recent default of Republic First Bank teach us, and what steps can risk managers and their supervisors take to mitigate or even ward off future fiascoes?

Friday, May 24, 2024

By Alla Gil


The regional bank crisis in the U.S. is far from being over. Last month, Republic First Bank was seized by Pennsylvania regulators, and Federal Reserve Chair Jerome Powell expects to see more banks fail due to their exposure to the commercial real estate (CRE) sector, which has floundered in the aftermath of the COVID-19 pandemic.

Many community banks have substantial exposures to this asset class because of the “credit paradox” – meaning that, unlike big banks, they can’t lend outside of their area of expertise, both with regards to product and geography.

alla-gilAlla Gil

Indeed, rather than systemically important giants, it's small- and medium-sized banks are in the most danger. And there could be hundreds of them.

Of course, not every CRE exposure is equally risky. This raises questions on how to identify the next potential regional bank failure and what can be done to mitigate exposure when it does occur.

Banks and their supervisors, in short, must analyze whether they have enough capital and liquidity to sustain extreme market volatility. Moreover, they must figure out the specific market factors to which their organizations are most vulnerable.

Subsequently, they should develop a plan for mitigating these risks and how to communicate their decisions – both internally and externally, based on transparent, data-driven analysis.

Lessons Learned from Republic First

When regional banks ponder whether they have enough capital and liquidity to survive a tail risk event, and what market factors may present them with the greatest risk, understanding what went wrong at the recently defaulted Republic First could prove quite helpful.

What were the key risk factors and risk drivers at Republic First? Is there any key data that should have been discovered in advance of that default? What scenarios should have been considered, and how should the bank have communicated with stakeholders after finding out it was in trouble?

Identifying the drivers of the main culprits of Republic First’s capital decreases and deposit runoffs (i.e., net charge-off levels and behavioral patterns of demand deposits) is the first step in this process. Historical time series of these segments are available in the FDIC database of the bank’s call reports, and we use this data for all the charts cited below.

The chart in Figure 1 shows the fit of First Republic’s net charge-off segment using the regression with regularization method. The table on the right lists the explanatory variables selected through exhaustive cross validation, as well as the lag of risk drivers and the coefficients of the regression fit.



Figure 2 shows a similar historical fit for the demand deposits segment.



Both demand deposits and risk drivers at Republic First were fueled by housing prices, CRE, inflation indicators and general charge-off rates on loans secured by real estate. Behavioral patterns for demand deposits were also driven by mortgage rates and 10-year treasury yield.

Generating the full range of macroeconomic and market scenarios at Republic First is the next step. When creating these scenarios, we need to start in early March 2023, before data on SVB and other defaults became available.

Scenario generation includes simulating thousands of consistent future values of the drivers listed in Figures 1 and 2. Shock events and their consequences must be incorporated. Subsequently, the segments under considerations must be projected – using the constructed equations with their lags and coefficients. (Through this approach, we can project net charge-offs and demand deposits on all future scenarios.)

Figure 3 (below) illustrates that, over the past year, there was a fairly high probability of a significant increase in the net charge-offs at Republic First.



In figure 4, a similar analysis shows that even though a substantial decline in deposit volumes exceeded expectation at Republic First, it was well within the 95th percentile of the constructed forward-looking distribution of outcomes.



Lastly, as shown in Figure 5, we can perform exhaustive reverse stress testing to analyze the reasons for projected adverse outcomes.



The red lines in Figure 5 show the 1st percentile worst-case projected outcomes for the demand deposits, one year (four quarters) from the scenario origination date (March 2023). Macro and market drivers are also depicted – and are highly correlated specifically with these outcomes.

As one can see, there is no perfect match between the Republic First drivers selected by the regression (Figure 2) and the ones shown in Figure 5. This misalignment occurs because there is a substantial change in correlations under stressful market conditions.

Our analysis further shows that the BBB-rated corporate yield didn’t significantly influence the behavior of demand deposits in normal times – but became very highly correlated with such behavior in 1st-percentile tails.

The Impact of Historical and Hypothetical Shocks

The market environment leading to Republic First’s depleted deposits — and, furthermore, to a liquidity crunch – was characterized by inflation when both corporate yields and interest rates were high. Digging deeper into one of the scenarios leading to the adverse outcomes, we can see that the Q4 2023 dip in demand deposits at Republic First was preceded by two market shocks (see Figure 6).



The shocks highlighted in blue represent historically observed events (like inflation and recessions), calibrated to the available data. The shocks highlighted in yellow are hypothetical (like geopolitical events and natural disasters), calibrated based on analogies and expert opinions – or implied from market data, such as country credit default swap spreads.

The horizontal axis in Figure 6 represents a forward-looking (quarterly) timeline. The quarter highlighted in green (Q4) marks the horizon at which key performance indicator (KPI) outcomes are analyzed. Yellow marks show earlier quarters, where risk drivers are highly correlated with KPI outcomes from Q4.

Matching earlier findings, mortgage rates exhibit high correlation in all kinds of environments. On the other hand, this holds true for BBB yield only under adverse outcomes. BBB yield can therefore be used as a trigger for conditional management actions, such as a combination of raising rates on demand deposits, overlaying strategic hedges, and pre-emptively boosting liquidity buffers.

Parting Thoughts

Banks that want to be fully prepared for any future market curveballs must: (1) find the true drivers of risky outcomes, even in unprecedented stress environments; (2) project their KPIs on the full range of scenarios, with all feasible combinations of shocks and their consequences; and (3) perform exhaustive reverse stress testing to discover the sources of adverse outcomes and their early warning indicators, and then construct relevant mitigation strategies.

This process consists of transparent and explainable steps, making it easier to communicate business and risk management decisions to all stakeholders.


Alla Gil is co-founder and CEO of Straterix, which provides unique scenario tools for strategic planning and risk management. Prior to forming Straterix, Gil was the global head of Strategic Advisory at Goldman Sachs, Citigroup, and Nomura, where she advised financial institutions and corporations on stress testing, economic capital, ALM, long-term risk projections and optimal capital allocation.


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