How can your financial institution prepare, but more importantly, thrive in an uncertain risk environment? Over the past year, banks and credit unions have experienced changes in how they go about managing liquidity and interest rate risk, while also considering what asset liability committees (ALCOs) and examiners may be looking for.
During the pandemic, liquidity was not an issue for most financial institutions because of the massive influx of deposits, and due to the low interest rate environment, many invested in longer-term securities. However, as interest rates have risen over the last year, the value of these investments has declined, and many banks have decided to reclassify to held-to-maturity (HTM) securities to mitigate mark-to-market earnings impacts, which made them relatively illiquid. With the recent bank failures creating fears regarding our banking system, we believe that liquidity oversight and stress-testing requirements will be a huge focus within the banking industry for the remainder of 2023 and beyond.
Asset liability management (ALM) is the process of managing financial risks that result from mismatches between assets and liabilities and the timing of cash flows. The concept of ALM focuses on the timing of cash flows and ensures that assets are available to pay debts as they come due. Changes in interest rates and illiquidity can cause a mismatch between assets and liabilities, and ALM helps to ensure that this does not occur. ALM is a regulatory requirement for both banks and credit unions to monitor interest rate risk, and it focuses on short-term (earnings) and long-term (market value of equity) risks. It is common to run tests quarterly and look at trends in order to gauge the current risk environment and its potential impact on cash flows.
By calculating the economic value of equity (EVE), financial institutions can measure long-term interest rate risk by measuring the fair value of its assets and liabilities over their respective terms. In other words, EVE is calculated by taking the present value of all asset cash flows and subtracting the present value of all liability cash flows. This is a long-term economic measure used to assess the degree of interest rate risk exposure, whereas net interest income (NII) reflects short-term interest rate risk.
As an earnings simulation, NII scenarios project future earnings on assets and cost of funds on its liabilities. This is calculated by subtracting the interest a bank must pay to its clients from the revenue it generates. The amount of NII a bank generates will depend on many factors, including the composition of its balance sheet, quality of the loan portfolio and the collective interest rates each type of loan carries.
ALM’s most common stress tests are changes in interest rates, known as interest rate shocks, because they have a large impact on cash flows. Most ALM models run monthly cash flow projections based on contractual instrument data – loans, investments, deposits, borrowings and assumption data. They model cash flows over each instrument’s life in order to get a full balance sheet cash flow stream. Cash flows are aggregated for purposes of ALM policy metrics.
The pandemic played a large role in impacting balance sheets and today’s interest rate environment. More specifically, it has greatly affected market rates and prepayment rates, which went from a short-term high to a short-term low in a matter of only 24 months, greatly increased interest rate volatility and had a huge impact on cash flow.
An ALM model can be used for more than just standard interest rate risk measurement – it is a cash flow tool at its core. A scenario analysis involves looking toward the future and visualizing what events or conditions are probable, what their consequences or effect may be and how to respond to or benefit from them. Stress testing is used as a complement to a scenario analysis – it focuses on the direct impact of a change in either one specific event or multiple events under extreme circumstances instead of focusing on changes on a more normal scale. Essentially, stress testing can measure the impact of low likelihood, but high-impact events. For example, stress testing uninsured and insured FDIC deposits, considering the size of deposits and historical behaviors related to those higher balance deposits.
On the other hand, reverse stress testing starts from the presumption of failure and seeks to identify the circumstances in which this type of failure could occur. In such cases, we would ask what outcomes could bring an organization to the brink of failure and then ask what could cause those outcomes. These models ultimately connect the dots between interest rate risk (IRR) modeling, budget analysis and its effect on liquidity. Banks continue to increase frequency of model validations especially around ALM and liquidity as part of their annual model risk management process.
Incorporating growth into the balance sheet to gauge drivers of interest income and interest expense can lead to identifying profitable lending strategies, funding sources and strategies and impacts to short-term and long-term liquidity. In terms of balance sheet projections, they can be described as “static” or “dynamic”:
Static balance sheet (SBS):
Dynamic balance sheets (DBS):
The large spike in deposit growth that has occurred in recent years has impacted liquidity levels significantly across the banking industry. Nonmaturity deposits are a key driver of scenario modeling because they are assumption driven – there are no certain cash flows and no structured term for nonmaturing deposits. To forecast future cash flows, banks need to look at the recent and historical behavior of deposits. Has there been an increase in average balances, or decline? What has been the historical growth of these deposits, and do we believe surge deposits reside in our portfolio?
It is vital to analyze interest expense increase year over year, test various term strategies to optimize net interest margin (NIM) and determine whether the asset side of the balance sheet is changing or staying the same. Utilizing data analytics is key in maintaining deposits and analyzing concentration risk – the concentration of liabilities on a bank’s portfolio.
After recent events, liquidity management has become a huge focus and contingency funding plans are proving to be essential. Banks must be able to demonstrate that they understand their current and future liquidity position and understand what risks they are facing. They should consider setting a system in place that will help in identifying liquidity risk and measure projected cash flows across the balance sheet to identify liquidity surplus and deficits. A liquidity stress test will assist in analyzing both short- and long-term liquidity risk. Deposits are key to liquidity stress tests, though they are difficult to forecast. Loan and investment payments are typically contractual and can be forecasted easily. Banks should also consider developing an early warning system and setting a contingency funding plan in place to manage liquidity should a triggering event occur.
ALM and liquidity are two essential parts of model risk management. For more information on these subjects, refer to the recording from our recent webinar, Leveraging your data to understand your liquidity and interest rate risk: a Compliance+ webinar. If you have any questions regarding ALM and handling your organization’s liquidity models, including a review or model validation of your bank’s current ALM and liquidity process, schedule a 30-minute meeting with one of our Value Architects™.
Register now for our next Compliance+ webinar, Crossing the CECL finish line with a model validation, taking place on Wednesday, April 19, at 11:30 a.m. ET.