Gap Analysis: The Original IRR Tool

What Is Gap Analysis?

Gap analysis is the simplest tool for measuring interest rate risk. It answers one question: "At what times do my assets and liabilities reprice, and what's the net exposure?"

It's been around since the 1970s, and despite its simplicity, it's still the foundation of how most banks talk about IRR. Regulators use it, boards understand it, and it's the first model most new ALM analysts encounter.

How It Works

Gap analysis starts by organizing your balance sheet into repricing buckets — time periods when assets and liabilities are expected to change interest rates.

Typical buckets:

• 0–1 month


• 1–3 months


• 3–6 months


• 6 months–1 year


• 1–2 years


• 2–5 years


• 5–10 years


• >10 years

For each bucket, you calculate:

Rate Repricing Gap = Assets repricing in bucket - Liabilities repricing in bucket

If the gap is positive (more assets repricing than liabilities), you benefit when rates rise and are hurt when rates fall. If the gap is negative (more liabilities repricing than assets), you're hurt when rates rise.

Practical Example

Imagine this simplified balance sheet:

| Item | Amount | Repricing |
|------|--------|----------|
| Assets | | |
| Fixed-rate mortgages | $50B | >10 years |
| Floating-rate C&I loans (SOFR + 2.5%) | $20B | 3 months |
| Securities (2-5 year Treasuries) | $15B | 1-2 years |
| Cash | $5B | Daily (reprices immediately) |
| Liabilities | | |
| Deposits (non-interest-bearing) | $30B | Non-repricing |
| Money market deposits (~30bp to SOFR) | $30B | 1 month |
| CDs (1-year, fixed) | $20B | 1 year |
| Wholesale funding (3-month LIBOR + 90bps) | $15B | 3 months |
| Equity | $10B | — |

Now bucket the repricing:

| Repricing Bucket | Assets | Liabilities | Gap |
|---|---|---|---|
| 0-1 month | $5B (cash) + $30B (MMA) = $35B | $30B (NIB) = $30B | +$5B |
| 1-3 months | $20B (C&I) + (part of MMA) = ~$20B | $15B (wholesale) = $15B | +$5B |
| 3-6 months | — | (part of CDs) = ~$5B | -$5B |
| 6-12 months | — | (rest of CDs) = ~$15B | -$15B |
| 1-2 years | $15B (securities) | — | +$15B |
| 2-5 years | — | — | — |
| 5-10 years | — | — | — |
| >10 years | $50B (mortgages) | — | +$50B |

Cumulative gap (often presented for easy reading):

| Period | Cumulative Gap |
|---|---|
| 0-1 month | +$5B |
| 0-3 months | +$10B |
| 0-6 months | +$5B |
| 0-12 months | -$10B |
| 0-24 months | +$5B |
| 0-10 years | +$5B |
| 0-infinite | +$50B |

Interpretation: You have a positive gap at 3 months (+$10B) and a negative gap at 12 months (-$10B). In the near term, you benefit from rising rates. In the medium term (6-12 months), you're exposed to rising rates.

The NII Impact Calculation

Once you have the gap, you can estimate the NII impact of a rate change:

Change in NII = Gap × Change in interest rates

Using our example, under a +100bp shock:

| Period | Gap | Rate Change | NII Impact |
|---|---|---|---|
| 0-1 month | +$5B | +100bp | +$50M (annualized) |
| 1-3 months | +$5B | +100bp | +$50M |
| 3-6 months | -$5B | +100bp | -$50M |
| 6-12 months | -$15B | +100bp | -$150M |

Total 12-month NII impact: ~-$100M (negative, because the negative gap at 6-12 months dominates).

If the bank's annual NII is $2B, this is a 5% decline in NII — well within acceptable tolerances for most banks.

Why Gap Analysis Is Beautiful (and Why It's Limited)

Strengths

1. Intuitive: Anyone can understand a repricing gap. You don't need a PhD in finance.
2. Easy to calculate: You can build a gap analysis in Excel in a few hours.
3. Clear governance: Boards can set a simple target: "Gap at 12 months should be between -$10B and +$10B."
4. Foundation for other models: Gap analysis is the baseline; more sophisticated models build on it.
5. Regulatory comfort: Regulators understand gap analysis. It shows up in call reports and stress testing frameworks.

Limitations

1. Assumes static repricing: A 1-year CD is assumed to reprice on its maturity date. But in reality, if rates are attractive, you might lose the deposit before maturity (prepayment risk). If rates are unattractive, you might extend it (renewal risk). The "1-year" assumption is a simplification.

2. Ignores embedded options: A mortgage with a prepayment option isn't correctly modeled as repricing in 30 years. When rates fall, mortgagors refinance, and that 30-year asset becomes a 6-month liability. Gap analysis misses this.

3. Ignores deposit stickiness: A non-interest-bearing deposit is modeled as non-repricing, which is technically true. But in practice, if rates rise 200bp and you don't raise your offering (because you're lazy or profitable), depositors leave. The "non-repricing" assumption breaks down.

4. Doesn't capture basis risk: Your loans reprice off SOFR, but your deposits reprice off Fed funds. There's a 5–10bp basis spread that varies. Gap analysis assumes the basis is constant.

5. Misses convexity: A symmetric ±100bp shock doesn't produce symmetric NII results. But gap analysis treats both directions the same.

6. No duration perspective: A 2-year repricing period is not the same as a 2-year duration. Duration accounts for reinvestment risk and convexity. Gap analysis doesn't.

How Banks Actually Use Gap Analysis

As a Screening Tool

Most ALM teams use gap analysis as a first-pass sanity check. If your 12-month cumulative gap is -$50B in a $200B balance sheet, you've got a problem. Gap analysis catches obvious mismatches quickly.

In Regulatory Reporting

The Federal Reserve's call report (Form FR Y-9C) requires banks to disclose repricing schedules in Schedules HC-R and HC-RI. These are gap analysis tables. Regulators and investors use them to benchmark your IRR profile.

For Board Communication

Most boards prefer gap analysis to more complex models. A chart showing cumulative repricing gaps over time is clear and actionable. "We have a +$15B gap at 0-3 months, which means we earn the float if rates rise, but we're exposed from months 3–12." That's language a board understands.

As a Constraint

Many banks set explicit gap constraints:

• "0-3 month cumulative gap: +/- $10B maximum"


• "3-12 month gap: +/- $15B maximum"


• "Cumulative gap to infinity: balanced (near zero)"

These constraints then drive liquidity management, asset/liability pricing, and business line targets.

The Evolution to More Sophisticated Models

Gap analysis has obvious limitations, which is why modern ALM teams also use:

• NII simulation (Module 33): Projects actual NII under multiple rate paths and behavioral scenarios, moving beyond static repricing assumptions.
• Key rate duration (Module 37): Captures how non-parallel curve movements affect both assets and liabilities.
• Economic value of equity (Module 36): Measures the true present-value impact of rate changes, not just 12-month earnings.
• Duration and convexity (Module 39): Explains why duration is more accurate than gap analysis for longer-term positions.

But gap analysis remains the foundation. Even banks with sophisticated models use gap tables internally. It's the lingua franca of IRR.

Key Takeaways

• Gap analysis is simple: organize your balance sheet by repricing dates and calculate the net asset gap.
• Use it as a screening tool and for board communication.
• Understand its limitations: it ignores options, basis risk, behavioral dynamics, and convexity.
• Don't rely on it alone. Supplement gap analysis with NII simulation, duration analysis, and EVE modeling.
• When you see a peer bank's gap table in their 10-K, you should be able to quickly assess their IRR profile.

In the next modules, we'll build on gap analysis with the more sophisticated tools that handle its limitations.

Gap Analysis: The Original IRR Tool — Deep Dive

Gap analysis is the oldest and most fundamental tool in the interest rate risk manager's toolkit. Before computers could run sophisticated simulations, before derivatives existed to hedge mismatches, before anyone had heard of key-rate duration, bankers managed interest rate risk by asking a simple but powerful question: "When does my money need to be repriced?" Gap analysis answers that question with elegant simplicity, though as we'll see, that simplicity masks considerable complexity when you dig below the surface.

This module explores gap analysis from its mathematical foundations through its practical limitations and evolution into the more sophisticated tools we'll examine in subsequent modules. Understanding gap analysis deeply is important because it remains the conceptual foundation for modern interest rate risk management, even as we layer more sophisticated approaches on top.

Part 1: The Mathematics of Gap Analysis

The Simple Gap Formula and Its Assumptions

The fundamental equation of gap analysis is deceptively simple:

Δ NII = GAP × Δ r

This elegant formula states that the change in net interest income equals the repricing gap multiplied by the change in interest rates. Where:

• Δ NII = the change in net interest income over a given period


• GAP = the repricing gap (assets repricing minus liabilities repricing in that period)


• Δ r = the change in interest rates

To understand why this formula works, consider what happens under a rate increase. Imagine you have assets that will reprice upward by a total of $100M (say, $50M in floating-rate loans that reset quarterly, and $50M in maturing loans that will be re-originated at higher rates). Simultaneously, your liabilities that will reprice are only $60M (deposits that will need to be repriced or renewed). The gap is positive: +$40M.

Now if rates rise 100 basis points, that $40M gap will earn an extra 100bp of spread, or $400,000 in additional annual NII. This is the gap formula in action: a $40M gap multiplied by a 100bp move produces a $400K earnings impact.

The beauty of this formula lies in its simplicity, but that beauty comes with important caveats. The formula assumes several things that are only approximately true:

1. The repricing occurs exactly as scheduled: The borrower will indeed refinance at the assumed time, not earlier, not later.
2. The interest rate change is instantaneous and permanent: Rates don't move gradually; they jump and stay at the new level. This matters because if rates phase in, the timing of repricing changes the impact.
3. All other variables remain constant: Volumes don't change (no prepayments, no deposits fleeing), spreads don't widen or tighten, and no customer behavior changes. In reality, all of these do change when rates move significantly.
4. The relationship is linear: A 100bp change produces exactly 100 times the impact of a 1bp change. In truth, there are non-linearities (convexity) that become important for large moves.

For most banks, these assumptions are reasonable for small shocks (plus or minus 25–50 basis points) over short horizons (1–3 months). But for large shocks (more than 100bp) or long horizons (more than 12 months), the breakdown becomes material and must be addressed through more sophisticated models.

Cumulative vs. Incremental Gap: Two Views of the Same Data

Gap analysis can be presented in two complementary ways, each providing different insights:

Incremental Gap focuses on a specific repricing bucket. For example, "In the 3-6 month bucket, assets repricing are $15B (maturing floating-rate loans), while liabilities repricing are $10B (CDs maturing in 4–5 months, roughly in the middle of this bucket). Incremental gap = +$5B." This view is useful for understanding what's happening in specific time windows but can be misleading if you don't understand how the buckets aggregate.

Cumulative Gap tells you the sum of all gaps from the beginning up to a given point. For example, "Through the 6-month bucket, cumulative gap = +$20B." This is more useful for IRR analysis because it captures the net repricing exposure over the entire period from today to 6 months from now. It tells you: if rates rose immediately and stayed at the higher level for 6 months while all assets and liabilities repriced as scheduled, the bank would have a positive $20B earning advantage because you've repriced more assets than liabilities.

The cumulative approach is more intuitive for IRR because what matters for earnings is the net repricing position over a horizon, not the bucket-by-bucket details.

Multi-Period Weighted Gap: Adding a Dimension of Sophistication

For longer-horizon analyses or when evaluating duration risk, banks sometimes employ weighted gaps that account for the timing of repricing within a bucket:

Weighted Gap = Σ (Gap_i × Time_i)

Where Time_i is the midpoint (in years) of repricing bucket i. This calculation produces a measure in "dollar-years," which approximates the duration characteristics of the gap.

Consider this example:

| Repricing Bucket | Gap | Midpoint (Years) | Weighted Gap |
|---|---|---|---|
| 0-1 month | +$5B | 0.042 | +$0.21B |
| 1-3 months | +$5B | 0.167 | +$0.84B |
| 3-6 months | -$5B | 0.375 | -$1.88B |
| 6-12 months | -$15B | 0.75 | -$11.25B |
| 1-2 years | +$15B | 1.5 | +$22.50B |
| 2-5 years | $0 | 3.5 | $0 |
| Total Weighted Gap | — | — | +$9.42B-years |

This weighted gap of +$9.42B-years suggests that the portfolio has a positive duration-like exposure to rising rates (you're positioned to benefit if the long end of the curve moves up faster). On a $100B balance sheet, this weighted gap implies roughly +0.09 years of duration risk, or about 1 year of a $9B position.

While weighted gap is useful, it's ultimately a simplified duration proxy. Modern banks use actual duration calculations and key-rate duration (covered in Module 37) for more precise exposure measurement.

The Assumption of Linear Repricing: Where Theory Meets Reality

Gap analysis assumes that an asset or liability reprices on a fixed date at a fixed spread. This is a useful fiction, but understanding where it breaks down is crucial to understanding gap analysis's limitations.

Consider a floating-rate C&I loan with quarterly repricing at SOFR + 250 basis points:

• Original terms: $10M outstanding


• Current quarter: SOFR is 5.25%, so the rate is 7.75%


• In 3 months: SOFR reprices (unknown today; the market is trying to guess the future path). Let's assume it goes to 5.50%, making the rate 8.00%

Gap analysis says: "This loan reprices in 3 months." That's correct, but it masks an important truth: the size of the repricing is uncertain because SOFR is unknown. The gap model implicitly assumes you know what future SOFR will be. In reality, you don't. This introduces interest rate scenario risk into the analysis—the same repricing bucket can have different earnings impacts depending on what future rates actually are.

Now consider a fixed-rate mortgage:

• Original terms: $250K, 6.5% fixed, 30-year amortizing


• Gap analysis says: "This reprices in 30 years."


• Reality: If rates fall to 4% in 5 years, the borrower refinances, and you lose the asset entirely. If rates rise to 8%, the borrower stays, and you keep earning 6.5% while competitors originate at 8%.

Gap analysis completely misses prepayment optionality. It assumes fixed dates, but embedded options in mortgages (and to a lesser extent, deposits and loans) mean repricing dates are uncertain. This is one reason gap analysis alone is insufficient for modern IRR management.

Part 2: The Behavioral Complications

Where gap analysis begins to break down is in the treatment of customer behavior. Rates don't just mechanically change; customers respond. A deposit rate that was competitive at one level becomes uncompetitive at another. A loan that was meant to be paid over 30 years gets prepaid in 5. Understanding these behavioral dynamics is essential to moving from gap analysis to more realistic simulation.

Deposit Repricing and the Beta Problem

This is perhaps the trickiest issue in modern gap analysis: how do you model deposits that don't have stated maturity dates?

A deposit beta is defined as the percentage of a market rate change that is passed through to the customer. For example, if you offer a savings account paying 4.50% when SOFR is 5.25%, and then SOFR rises to 5.50% (a 25-basis-point increase), and you raise the savings rate to 4.70% (a 20-basis-point increase), your deposit beta on this product is 20bp / 25bp = 0.80, or 80%.

Deposit betas are crucial for gap analysis because they determine when deposits effectively reprice. But betas are not constant. They depend on multiple factors that can shift over time:

1. Competitive environment: In a tight labor market with tech companies offering high deposit yields to retain talent, or when regional banks are desperately competing for market share, betas can exceed 100% (you move rates faster than the market to prevent outflows). In a slack environment with ample liquidity, betas can be 10–20%.

2. Absolute rate level: At zero or near-zero interest rates, further downward moves are impossible, so betas on deposit cuts are naturally zero. At higher rate levels (above 3–4%), betas tend to be higher as depositors actively monitor alternatives.

3. Direction of rate movement: Rising-rate and falling-rate environments produce different betas. When rates are rising, banks gradually raise deposit rates to retain deposits (typical beta 50–80%). When rates are falling, banks cut deposit rates slowly and incompletely because customers don't protest loudly about earning less (typical beta 20–40%). This asymmetry is a major complication.

4. Type of deposit product:
- Non-interest-bearing deposits: Beta is near zero (they earn nothing regardless of rates)
- Money market deposits (MMDAs): Beta is 80–100% (these are rate-sensitive and easily moved to competitors)
- Savings deposits: Beta is typically 40–60% (moderate rate sensitivity; some inertia)
- Certificates of Deposit (CDs): Beta = 100% at maturity (the rate is fixed at origination; when it matures, you pay whatever the market rate is)
- Relationship deposits (tied to credit relationships): Beta might be 20–40% (customers tolerate lower rates in exchange for better lending terms)

5. Customer type: Institutional deposits (corporate treasurers, money managers) have higher betas (50–90%) because they actively shop rates and move money in response to small differences. Retail consumer deposits have lower betas (30–60%) due to inertia, brand loyalty, and switching costs.

Understanding these nuances is critical because they determine whether your $30B deposit base reprices in 3 months or 12 months, which in turn determines your gap exposure.

How Beta Affects Gap Analysis

Let's work through a concrete example to see how deposit betas flow through to gap calculations. Suppose you have a $30B deposit base that looks like this:

• $10B non-interest-bearing (beta 0%)


• $10B savings deposits (beta 50%)


• $10B money market deposits (beta 90%)

Under a hypothetical +100 basis-point rate shock in a rising-rate environment, what happens?

• Non-interest-bearing deposits: You keep earning the float (0bp increase), so they produce no cost increase


• Savings deposits: You raise rates 50bp to retain them, losing $50M per year in earnings (50bp on $10B)


• Money market deposits: You raise rates 90bp to retain them, losing $90M per year in earnings (90bp on $10B)

The effective beta for the entire $30B is: (0 + 50 + 90) / 3 / 100bp = 46.7%. You expect to lose about 47bp on the deposit book in a 100bp rising-rate environment.

Now comes the thorny question: When does this $30B of deposits "reprice" for gap analysis purposes?

The problem is that the $30B is heterogeneous. The $10B non-interest-bearing never reprices (beta 0%). The $10B in savings reprices partially and gradually (beta 50%, repricing over weeks to months). The $10B in money market reprices quickly (beta 90%, repricing over days to a week).

Gap analysis forces you to pick a single repricing date for the entire $30B. You have several options:

Option 1 (Conservative approach): Model all $30B as repricing in 1 month, assume worst-case beta of ~100%. This overstates your repricing exposure but is prudent for risk purposes because it assumes the most aggressive competition. Under this assumption, your gap analysis shows $30B of liabilities repricing in the 0-1 month bucket.

Option 2 (Realistic approach): Split the deposits by type and model each with its own repricing date and beta. Put $10B of non-interest-bearing in the "never reprices" bucket (treated as equity-like), $10B of savings in the "6-month" bucket with 50% effective repricing, and $10B of money market in the "1-month" bucket with 90% repricing. But this adds complexity.

Option 3 (Simplified approach): Model an effective beta of 47%, so the $30B is assumed to reprice at 47% of the rate change in a 3-month bucket. This is computationally simple but hides the heterogeneity.

Most large banks opt for Option 2: they split deposits by product type and maintain separate repricing assumptions for each. This is documented in the ALM policy so that the assumptions are explicit and defensible to regulators.

Non-Interest-Bearing Deposits: The Paradox

Non-interest-bearing deposits present a fascinating paradox that gap analysis struggles to handle. They account for 15–30% of a large bank's deposit base, making them economically crucial. Yet they don't have a stated "repricing" date.

Here's the dilemma: NIB deposits earn 0%, so they don't technically "reprice" if you only look at the stated rate. But from an economic perspective, their cost has changed dramatically.

When rates are near zero (as in 2020–2021), a non-interest-bearing deposit costing 0% is cheap funding. When rates rise to 5%+ (as in 2022–2023), that same 0% deposit is expensive funding because depositors know they could earn 5% elsewhere. The implicit economic cost of the deposit has risen 500bp, even though the stated rate hasn't moved.

Furthermore, there's an asymmetry:

• When rates rise 100bp: Depositors are tempted to move their money to earn 100bp more elsewhere. The implicit cost of your NIB deposits effectively rises by the full 100bp. To retain deposits, you might offer fee waivers, relationship discounts, or other non-rate benefits that are effectively a cost.

• When rates fall 100bp: Depositors can't move their money to earn more; they earn less everywhere. You experience a benefit from these "sticky" deposits that now cost less than market rates. But the benefit is asymmetric—you capture the full 100bp advantage.

This asymmetry is huge for EVE analysis (which we cover in Module 36) but is often ignored in simple gap analysis. Most banks handle NIB deposits by using an implicit beta of 30–50% in rising-rate scenarios (modeling the assumption that you lose some of the balance to attrition as rates rise and alternatives become attractive) and a 0% beta in falling-rate scenarios (modeling that deposits are sticky downward).

The Mortgage Prepayment Problem

Mortgages are typically the largest asset class for regional and community banks, often representing 40–60% of total assets. How they're modeled in gap analysis determines the entire IRR profile of the bank. Yet gap analysis's simple assumption—"this reprices in 30 years"—is wildly inaccurate.

Consider a realistic scenario: A bank originates a $100M portfolio of 30-year, 6.5% fixed-rate mortgages. Gap analysis says: "This reprices in 30 years." But then, one year later, interest rates fall to 4.5%. Suddenly, 80% of the borrowers refinance. Your $100M portfolio becomes a $20M portfolio. The actual repricing date was not 30 years; it was 1 year.

The prepayment function is the model that describes how likely borrowers are to refinance as a function of rates. The probability of refinancing (often expressed as a CPR, or Conditional Prepayment Rate) depends on:

• Refi incentive: The difference between the current market rate and the mortgage rate, usually expressed as a percentage: (Current Rate - Mortgage Rate) / Mortgage Rate. If current rates are 4.5% and the mortgage is 6.5%, the incentive is +2.0% (44% in the money). Strong refi incentives drive prepayments.

• Age of the loan (seasoning): Newer mortgages are less likely to refinance even if in-the-money (the borrower hasn't yet thought about it, or is in the early phase of the loan). Mortgages age 6 months to 3 years have the highest refinancing speeds. Very old mortgages (>15 years) have lower speeds because there are fewer years left to benefit.

• Economic conditions: In a strong labor market with low unemployment and confidence, homeowners are more likely to refinance (they're employed, can document income, can refinance). In a recession, refinancing slows even if rates are attractive.

• Origination channel and terms: Loans originated through wholesale brokers tend to have higher refinancing speeds (less personal relationships to keep the customer). Loans with recapture prevention agreements (where the bank bought back refinancing rights) have lower speeds. VA loans have different speeds than FHA loans.

A reasonable prepayment model might look like this:

| Refi Incentive | CPR (Conditional Prepayment Rate) |
|---|---|
| < -2% | 0.3% (some people refi for cash-out) |
| -2% to 0% | 5% |
| 0% to +1% | 15% |
| +1% to +2% | 35% |
| > +2% | 70% |

Where CPR is the annualized prepayment rate (the fraction of remaining principal expected to be paid off in a year).

Using this model, if rates fall and the refi incentive jumps to +2.5%, the CPR becomes 70%, meaning your 30-year mortgage asset has an effective life of roughly 2 years (most of it prepays by then). Gap analysis's 30-year assumption is off by a factor of 15, which completely distorts the IRR analysis.

Gap analysis cannot capture this prepayment behavior because it assumes fixed repricing dates. This is one of the key reasons modern IRM has moved beyond gap analysis to more sophisticated simulation approaches.

Part 3: How Real Banks Implement Gap Analysis

The Gap Report Template and Interpretation

Despite its limitations, gap analysis remains the fundamental IRR reporting tool at virtually every bank, from the Fed's supervisory perspective to internal ALM management. Here's what a realistic gap report looks like:

| Repricing Period | Assets ($000s) | Liabilities ($000s) | Gap ($000s) | Cumulative Gap |
|---|---|---|---|---|
| 0-1 month | 12,500 | 10,000 | 2,500 | 2,500 |
| 1-3 months | 8,000 | 5,000 | 3,000 | 5,500 |
| 3-6 months | 5,500 | 8,000 | (2,500) | 3,000 |
| 6-12 months | 6,000 | 12,000 | (6,000) | (3,000) |
| 1-2 years | 8,500 | 4,000 | 4,500 | 1,500 |
| 2-5 years | 12,000 | 3,000 | 9,000 | 10,500 |
| 5-10 years | 18,000 | 2,000 | 16,000 | 26,500 |
| >10 years | 128,000 | 6,000 | 122,000 | 148,500 |
| Total | 198,500 | 50,000 | — | — |

Reading this report requires understanding what each section tells you:

• Strong near-term positive gap (0-3 months: +$5.5B cumulative): The bank has more assets repricing than liabilities in the near term. If rates rise 50bp in the next 3 months, the bank gains approximately $27.5M in NII (50bp × $5.5B gap).

• Negative gap emerging at 6-12 months (-$3M cumulative gap through 12 months): The bank's repricing advantage disappears beyond 3 months and turns negative. This suggests that liabilities are repricing faster than assets in the medium term, creating vulnerability to rising rates if they persist beyond 3 months.

• Strong positive gap beyond 1 year (+$1.5B at 1-2 years, accelerating to +$10.5B at 2-5 years, and +$26.5B at 5-10 years): The bank has a long-duration asset base (mortgages, securities) that doesn't reprice, funded by short-duration liabilities. This is the core of the bank's interest rate risk: it's betting that rates won't rise significantly, because if they do, the economic value of the balance sheet deteriorates.

• The cumulative gap at infinity (+$148.5B, which equals equity): This is a sanity check. The total assets minus total liabilities must equal equity, and this works out: $148.5B gap represents the net position at all horizons, which is the shareholder's equity stake.

Systems, Classification Rules, and Governance

Most large banks implement gap analysis through their ALM information system, which is a specialized financial system (often built on platforms like Murex, Integrex, FI360, or internal systems) that maintains detailed repricing schedules for every asset and liability. The pipeline is:

1. Core banking system (e.g., FIS Fiserv, Jack Henry, etc.) provides daily updated balances and contractual terms for all deposits, loans, borrowings, and securities holdings
2. ALM system ingests this feed and automatically classifies each position into a repricing bucket based on:
- Contractual repricing date (maturity, rate reset date)
- Behavioral repricing assumptions (deposit betas, prepayment models, renewal expectations)
3. Gap analysis engine aggregates positions into standard buckets (0-1 month, 1-3 months, etc.) and calculates gaps
4. Reporting layer publishes daily, weekly, and monthly gap reports to ALM, risk, treasury, and (monthly) to the board

The classification rules are crucial and often contentious. Should a 3-month CD that's likely to renew at maturity be modeled as repricing in 3 months or 12 months? Should a non-interest-bearing deposit be modeled as repricing in 6 months (if you lose it to competitors) or never? Should a 10-year mortgage with a 5-year average life (due to prepayments) be modeled at 5 or 10 years?

Each choice changes the gap and the apparent risk profile. Good governance requires these rules to be explicitly documented in the ALM policy so they're transparent, defensible, and consistent over time.

Board Governance and Limit Setting

Most boards establish gap limits as part of formal ALM policy. These limits protect the bank from taking inadvertent risk. A typical policy might look like:

``
ALM Policy: Repricing Gap Limits

Cumulative gap through 3 months: Not to exceed +/- $15B
Cumulative gap through 12 months: Not to exceed +/- $20B
Cumulative gap at 2+ years: Not to exceed +/- $10B
``

Why these specific limits?

• 0-3 month limit (+/- $15B): Protects against near-term repricing shocks. A large positive gap means you're betting on rising rates over the next 3 months; a large negative gap means you're betting on falling rates. Limits force management to stay near neutral on the rate path unless intentionally hedged.

• 0-12 month limit (+/- $20B): Captures your medium-term earnings exposure, which is material for profit guidance and capital planning.

• 2+ year limit (+/- $10B): Captures your long-term duration risk. Ideally, you want to be roughly duration-matched to your equity base (long-term, stable value), not taking giant directional bets on what rates will do over years.

If gaps exceed these limits, ALM must take action: adjust deposit pricing to accelerate/slow repricing, shift loan origination mix toward floating or fixed rates, adjust security purchases, or issue/repay debt of specific tenors.

Board-level governance around these limits is important because they represent a commitment to a risk posture. If management repeatedly breaches limits and boards rubber-stamp it, the limits become meaningless.

Part 4: Limitations of Gap Analysis and the Path Forward

What Gap Analysis Doesn't Tell You

Basis Risk: Your assets might reprice off SOFR, but your liabilities reprice off Fed funds or something else. These rates move similarly but not identically. A 100bp SOFR move might be a 95bp Fed funds move. The basis (the spread between them) varies, creating a source of error in the gap calculation.

Correlation Risk: Assets and liabilities might move at different speeds depending on market conditions. In normal times, the correlation is high (>0.95). But in crisis periods (when central banks intervene), correlations can break down. Gap analysis assumes perfect correlation.

Convexity: The gain from a -100bp shock is not the same as the loss from a +100bp shock. Deposits have a floor (you can't pay negative rates), mortgages have prepayment options, and there are non-linearities throughout. Gap analysis treats both directions as identical.

Behavioral Uncertainty: Prepayments, deposit flight, origination volume elasticity—all these depend on what actually happens, and history is an imperfect guide. Different scenarios might require different assumptions, but gap analysis treats the repricing dates as fixed.

Option Risk: Embedded options (mortgage prepayments, deposit optionality, loan acceleration clauses) aren't captured. Gap analysis assumes a borrower will hold a mortgage for 30 years, which misses the fact that they'll prepay when it becomes economic to do so.

Where Gap Analysis Remains Valuable

Despite its limitations, gap analysis survives as a foundational tool because it:

• Provides intuitive understanding: A non-specialist can look at a gap report and understand the basic repricing mismatch.
• Enables quick screening: You can identify gross imbalances in repricing profiles without running complex simulations.
• Supports regulatory compliance: Gap reporting is a standard supervisory expectation.
• Guides other models: Gap analysis is the input to more sophisticated simulations; it tells you the basic repricing structure that you then layer behavioral complexity on top of.

The Evolution to Simulation

Modern ALM teams use gap analysis as a foundation but layer on more sophisticated tools:

Gap analysis (Module 32): "When do things reprice, and by how much?"

NII simulation (Modules 33): "What does my actual earnings look like under different rate scenarios, accounting for behavior?"

EVE simulation (Module 36): "What's the true economic value impact of rate moves, including long-term effects?"

Key rate duration (Module 37): "Which maturity buckets am I most exposed to, and what happens if the curve moves non-parallel?"

Together, these frameworks give ALM a complete picture of interest rate risk. In the next module, we'll dive into NII simulation, where the real complexity begins.