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ALM 101: Introduction to asset/liability management – Part 2: Interest rate risk – earnings at risk

February 24, 2022
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ALM & measuring short-term interest rate risk

Interest rate risk is measured through two approaches. This ALM 101 post describes the earnings at risk(EAR)/income at (IAR) risk perspective (short-term risk to the income statement).

Access all of the articles in this ALM 101 series. Download the eBook


Earnings at Risk

Measuring interest rate risk accurately

As described in the first post of this series, a key component of effective asset/liability management (ALM) is managing risks. Taking on risk is a necessity to generate the returns needed to hit desired financial goals.

However, risk comes in many different shapes and sizes, and each institution is going to have its own unique appetite for each type of risk. One of these risks is a significant one: interest rate risk (IRR).


ALM 101 blog series

ALM 101: Introduction to Asset/Liability Management

The Federal Reserve’s upcoming rate hike gives bankers more reason than ever to focus on IRR, asset/liability management (ALM), and deposit management strategies, policies, and programs. This post, the second in the series, dives into sources of interest rate risk. It also describes how financial institutions can make sure they measure interest rate risk accurately.

Defining interest rate risk

A loose definition of interest rate risk is the risk that an institution’s earnings and market value may decline as market interest rates change. 

Interest rate risk originates from the difference in a financial institution’s expected cash flows vs. its actual cash flows. What creates this difference between expected and actual cash flows?

Institutions secure financial instruments at a certain rate based on the market rates as of a specific point in time.  Expected cash flows are developed based on the maturity/repricing (and option) structure of those instruments. However, market rates change over time, and that can affect cash flows, introducing risk to projected earnings.  Additionally, changes in market rates affect the value of instruments, and ultimately the economic value of a financial institution’s equity.


Approaches for measuring IRR

Regulators expect a well-managed financial institution to look at interest rate risk through two different lenses:

  • Earnings at risk (EAR)/income at risk (IAR) – Measures short-term risk; changes to the income statement.
  • Value at risk(VAR)/economic value of equity (EVE) – Measures long-term risk; the change in value of instruments, and ultimately the potential for long-term earnings.

Both earnings at risk and economic value of equity measure the impact on earnings, but the time horizon of each metric is the differentiator and informs management of two separate, but equally important measurements. Another article in this series will expand on the value at risk perspective, but below is a focus on the earnings/income at risk perspective.

With the earnings at risk analysis, the goal is to measure the impact on net interest income (NII) resulting from movements in market rates, which impacts an institution’s return on assets (ROA) and ultimately shareholder returns, or return on equity (ROE).

Even small changes to an institution’s NII can have significant impact on ROA and ROE, which is why managing and monitoring all aspects of interest rate risk is paramount in ALM modeling.

Interest rate risk requirements

Prudential regulators expect financial institutions to measure the level of potential interest rate risk in their portfolios. In 1997, all members of the Federal Financial Institution Examination Council (FFIEC) except the National Credit Union Administration, added the “S” (Sensitivity to Market Risk) measure to the CAMELS rating to account for interest rate sensitivity within institutions.

For institutions to receive a “Well Managed” rating for Sensitivity, they must:

  • Measure both EAR and EVE
  • Extend simulation of earnings at risk to at least two years
  • Run a static balance sheet for comparison if the institution is using dynamic balance sheet modeling (see below)

For credit unions, the NCUA in 2021 finalized a rule adding the “S” component to its CAMEL rating system to clearly distinguish between evaluating a credit union’s sensitivity to interest rate risk and the liquidity risk (which is evaluated by a revised “L” component in the ratings). The rule is effective for credit union examinations starting on or after April 1, 2022, and examiners will focus on compliance with this component in upcoming exams, according to the NCUA’s 2022 Supervisory Priorities.

While there are minimum regulatory requirements around interest rate risk, the asset/liability management solutions that allow for the best strategic decision-making do not settle for the regulatory minimum.

4 Areas

Sources of interest rate risk

A comprehensive interest rate risk model considers all four areas of interest rate risk, as identified by the Joint Agency Policy Statement. These sources of interest rate risk are:

Repricing risk

The impact on earnings of assets and liabilities repricing at different times or amounts. Some examples include:

  • Maturity: A CD yielding 1% maturing. If the institution wants to retain these funds, it will have to bring it back on the books at a rate that may be different than 1%.
  • Amortization: A 30-year mortgage that reprices gradually as payments are received.
  • Prepayment: A loan at 6% with market rates at 4%. The loan reprices when the borrower refinances to a better rate.
  • Contractual Repricing Terms: An adjustable-rate mortgage repricing to market rates every year

Basis risk

The difference in market-rate movements among various indices driving rates. For example, the rate on a 3-month Treasury bill increases by 50 bps, but the 3-month LIBOR only increases by 25 bps.

Yield curve risk

The movement of different maturities on the yield curve. For example, the 3-month Treasury curve increases by 50 bps, but the 10-year Treasury curve only increases by 25 bps, representing a move toward a flattening yield curve.

Option risk

Changes to cash flows resulting from rate movements. For example, a borrower decides to refinance their mortgage due to a decrease in market rates.

Some questions to ask regarding your model or interest rate risk software:

  • Does our model incorporate all four areas of interest rate risk?
  • If not, what risks are we failing to measure?
  • What changes need to be made within our modeling to encapsulate all areas of potential risk?

When going through this exercise, remember the relationship between risk and return. This shouldn’t just be a preventative “check the box” exercise to eliminate risk, but rather a holistic look as to whether you’re considering all potential risks in strategies and that they don’t outweigh potential returns.

Dynamic vs. static modeling

Changing from a “static” to a “dynamic” ALM modeling approach will remedy several common gaps in IRR modeling. A static modeling approach only considers the current state of the institution’s portfolio. In contrast, dynamic modeling also takes into account any changes to the future balance sheet, such as strategy or growth targets.

Since static modeling only looks at the existing balance sheet, it can only answer the question of “what risks are present today?” Dynamic modeling can evaluate risk/return trade-offs in the institution’s strategy. If your institution is planning on growing fixed-rate mortgages by 10% over the next year, how can you manage the risks of that strategy and evaluate whether the potential return is worth it if that additional 10% is not considered in your IRR model?

"Since static modeling only looks at the existing balance sheet, it can only answer the question of “what risks are present today?” Dynamic modeling can evaluate risk/return trade-offs in the institution’s strategy."

To get a realistic view of potential impacts of strategies, management should implement dynamic models to account for the future as well as today. However, if a dynamic model is used, a static model still needs to be run as a base case and for comparison, as growth can sometimes mask risk.

Measurement methods

Using gap analysis and income simulation

One traditional method of measuring short-term IRR is a gap analysis. This analysis measures the difference in cash flows between rate-sensitive assets and liabilities and identifies the “gap” where repricing of instruments could produce risk. While this is a good first step toward measuring earnings at risk, there are glaring shortcomings to a gap analysis.

There is no consideration for rate drivers or optionality, leaving significant holes in the gap analysis itself. In fact, looking back to the four different types of IRR (repricing, basis, yield curve, etc.), repricing risk is the only type of risk that gap analysis does an adequate job of measuring.

Another, and the most common and effective measurement method for calculating earnings at risk is an income simulation. An income simulation model measures the impact of interest rate changes on earnings by running the balance sheet through different interest rate scenarios and measuring the change in earnings from the “base case” (i.e., no change in market interest rates).

While this type of analysis may be called an income simulation, not all income simulation models are built the same. 

For an income simulation to be effective in measuring risk, it should meet the following requirements. It:

1. Shows the amount and direction of interest rate exposure under different interest rate scenarios

2. Captures all relevant cash flow and maturity and repricing data points, including important options, such as caps, floors, prepayment penalties, etc.

3. Clearly shows the impact of key variables on overall exposure levels

4. Must be a dynamic analysis

- Future balance sheet is considered along with the current balance sheet

- A static analysis must always be considered, according to regulations.

- Must be able to show how changing rates will impact each product differently (i.e., due to basis risk and yield curve risk)

5 Steps

Building out an effective income simulation

Building out an effective income simulation can be broken down into 5 steps:

1. Develop a base case

  • Start with current (static) balance sheet
  • Factor in any changes to growth/strategy
  • Develop a budget/plan as base case
  • No rate changes applied to this base case

2. Add a time horizon to model

  • Per regulatory requirements, this must be at least two years
  • Decide whether to measure each year individually or in total (“Will we look at Year 1 and Year 2 impacts separately or in aggregate?”)
  • Understand that longer horizons breed variance and less reliable results

3. Apply rate changes to model

  • How will rate changes be applied?
  • Typical examples include:
    • Immediate & Parallel (I&P) shocks: Rates move immediately and permanently, with parallel movements across all drivers
    • Gradual Rate Ramps: Rates gradually move to a certain point over a period of time
    • Economic Rate Projections: Actual forecast of rates
  • Are we considering increases and decreases in rates?
  • Are I&P and gradual rate ramps realistic? Should we be using those scenarios in our decision-making

4. Factor in changes in business conditions

  • How will our balance sheet change in different economic environments?
  • If economic scenario “X” happens, how will we respond in our product offerings?

5. Establish policy limits

  • For each scenario, establish a limit of acceptable decrease in earnings as compared to the established base case in Step 1.
  • Policy limits should be more conservative for less extreme scenarios (example: +/- 100 bp) but can be more lenient for more extreme cases (example: +/- 400 bp)

Once these assumptions are applied and the calculation is run, compare how overall earnings are impacted and note the change in earnings as a percentage from the base case projected. A few pertinent questions to consider are:

  • Are there any noticeable trends indicating significant sensitivity within the institution?
  • Are the scenarios where the institution is earning a return outpacing the scenarios where it may be at risk?
  • Based on policy limits, are there any scenarios that violate risk tolerances where the institution should re-evaluate planned growth, strategy, etc.?
  • What other relevant information are these scenarios showing?

These results and ensuing discussions help set up the conversation of future strategy and help effectively manage inherent risks.


Financial institutions have always had to manage interest rate risk. However, it can affect earnings and the underlying value of the institution’s balance sheet. Regulators understand that the products and services offered and the competitive environment have increased the importance of managing this risk wisely.

Some institutions find it helpful to utilize ALM consulting experts when structuring the model so that management can concentrate on enhancing the institution’s performance. Others choose to have the ALM model outsourced entirely.

Whether you control every aspect of your ALM model, outsource different steps, or utilize complete ALM outsourcing, financial institutions in this changing environment cannot afford to have a “check the box” mentality if they want to manage risk while generating the returns that will help them hit financial goals.

Read Part 3 of the ALM 101 Series: Interest Rate Risk - Value at Risk

You might also like this infographic: "5 Steps to building an effective income simulation"

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About the Author


Abrigo enables U.S. financial institutions to support their communities through technology that fights financial crime, grows loans and deposits, and optimizes risk. Abrigo’s platform centralizes the institution’s data, creates a digital user experience, ensures compliance, and delivers efficiency for scale and profitable growth. Make Big Things Happen.

Full Bio

About Abrigo

Abrigo enables U.S. financial institutions to support their communities through technology that fights financial crime, grows loans and deposits, and optimizes risk. Abrigo's platform centralizes the institution's data, creates a digital user experience, ensures compliance, and delivers efficiency for scale and profitable growth.

Make Big Things Happen.