Navigating Behavioral Assumptions and ALM Challenges Amid Economic Shifts

Since the start of the pandemic, government intervention and the subsequent rise in deposits, price increases, and deposit pricing increases have created new challenges in estimating assumptions critical to ALM.

What was the impact of COVID Stimulus?

• COVID stimulus was massive ­– totaling $12.2T, the COVID stimulus was nearly double the dollar-adjusted amount infused into the financial system during the 2008 financial crisis ($6.8T in 2020 dollars).
• Stimulus drove surge deposits – As a result of the stimulus there was a $4.6T increase in deposits held at Commercial Banks.  This high deposit base kept interest rates on both loans and deposits low and put financial institutions in a strong liquidity position.
• Inflation –Inflation started to spike because of low rates and easy money reaching a 40-year high of nearly 9% in 2022.  Fed Funds rate hikes started in 2022 to slow these price increases.
• Deposit runoff – As consumers and businesses struggled with high prices and rising rates, they started to draw down on deposits throughout 2022, putting financial institutions in a tight liquidity position in 2023 and into 2024.
• Interest rates follow inflation – In response to rising inflation, the Federal Reserve sharply increased interest rates, leading to a sharp decline in prepayment activity.

How did that impact the ability to calibrate behavioral assumptions?

Through the end of 2021, even a ten-year historical observation lookback would have contained only low and stable interest rates; not a sufficient sample for estimating deposit behavior in the current high and rising rate environment, nor for a potential falling rate environment in 2025.  However, incorporating 2022 and 2023 data into calibrations causes new issues:

Betas – Betas represent the correlation between deposit rates and market rates (Fed Funds).  The issue with the massive stimulus, however, was that when rates started increasing, banks had so many deposits that they didn’t have any need to raise rates.  For the first 200 bps of Fed Funds hikes in 2022, there were nearly no changes in rate.  Incorporating this period of historical data would significantly under-estimate betas.  On the contrary, when the Federal Reserve slowed hikes in 2023, there continued to be rises in deposit rates due to competition for deposits in a tight liquidity market. 

Decays – When financial institutions first started receiving COVID stimulus deposits, the conventional wisdom was that it was “hot money” and would leave the banks soon.  The money was considered non-stable and subject to high decay speeds.  This thinking softened as the deposits remained at the institutions well into 2022, and many risk managers started believing that this money was in fact stable, and any decay rate calibrations done during this time would have significantly underestimated potential decay, which were ultimately realized in early 2023.

Prepayments – The sharp rise in interest rates, particularly impacted by a historically low-rate environment, caused prepayment speeds to drop dramatically.  This impact was significant in the residential real estate market where elevated rates coupled with high home prices worked to reduce home affordability and drive loan demand even lower. 

How Can Financial Institutions Manage the Risk of These Parameters?

The most important thing to do in this period of uncertainty is to stay calm.  It is not the time to abandon best practices or quantitative approaches altogether.  Uncertainty is not a reason to keep stale estimates or to use static assumptions across rate shock scenarios.  Rates will continue to change, and decays, betas, stable balances, and prepayment speeds will continue to change along with them. 

Given the challenges with calibrating behavioral assumptions in this period of uncertainty, ALM managers must assume that the assumptions being used will not capture the exact behavior of borrowers and depositors as rates continue to evolve. The potential for error in the behavioral assumptions gives ALM managers another risk to consider.

Risk managers must carefully consider and enhance fundamental Model Risk Management approaches to risk forecasting.  Considering market and model uncertainty, it is more critical than ever for financial institutions to implement a methodical and consistently applied approach to managing model risk.  Four key approaches that all institutions should follow can significantly enhance management’s ability to rationalize and act based on ALM results:

1. Follow best practices for parameter calibration.  Keeping a methodical approach to calibration helps justify output with examiners, auditors, and senior management.
   
2. Document assumptions, methodology, and rationale.  Robust documentation assists with transfer of knowledge both internally and externally.
   
3. Monitor “realized parameters”.  The best way to be sure that calibrated parameters are performing as expected is to monitor actuals. “What is the ratio of deposit rate increases to Fed Funds increases over the last year”?
   
4. Stress-Test Assumptions. Running scenario analyses with up and down betas and decays is a great way to build confidence in your output.  Even if assumptions are wrong, knowing that the forecasted risk profile is acceptable under both 50% up and down beta shocks, management can feel more able to make decisions using the calibrated parameters.

By employing these best practices in Model Risk Management (MRM), financial institutions can better navigate uncertainty. These strategies empower risk managers to rationalize outputs, enhance decision-making, and maintain stability, even amid evolving market conditions.

Want to learn more? Empyrean’s latest webinar “Breaking Down the Stress Test: A Practical Approach to Capital Management” dives into Stress Testing, how it is being used by financial institutions to enhance strategic management.