Deposit betas measure the sensitivity of your deposit costs to market rate changes. A beta of 0.60 means: for every 100 basis points the market moves, your deposit costs move 60 basis points. This single number shapes your net interest margin, your ALM hedge ratios, your funding strategy, and ultimately your earnings stability.
Yet deposit betas are maddeningly hard to estimate and unstable. Historical betas from 2019 are worthless in 2023. Banks that estimated betas on pre-2022 data took massive margin surprises when rates rose and deposits repriced faster than model assumptions. The 2022–2024 rate cycle revealed this starkly: regional banks that had betas of 30–40% in calm markets discovered they had effective betas of 80–90% when spreads widened and competition intensified.
This module teaches you how to estimate deposit betas rigorously, how to track them in real time, and how to build the estimation logic that works across rate cycles. By the end, you'll have a working framework that captures structural changes in your deposit franchise without requiring you to rebuild your model every 18 months.
Why Betas Matter
One simple example: Assume you have $100B in deposits, a 5-year loan portfolio averaging 4.5% yield, and a 50% asset-to-deposit ratio in shorter-term debt (CDs, wholesale funding). Your net interest margin is:
NIM = (Loan Yield × Loan Assets + Short-Debt Yield × Short Debt) / Total Assets - (Deposit Beta × Market Rate Change × Deposit Base / Total Assets)
If the Fed raises rates 100 bps and your deposit beta is 0.40, your deposit costs rise 40 bps on average—a $400M earnings impact per year. If your beta is actually 0.70 (as it was for many banks in 2023), the hit is $700M—a $300M surprise miss. On a typical regional bank's pretax income of $1.5–2B, that's a 15–20% earnings shock.
This is why accurate beta estimation is not an academic exercise. It directly drives your interest rate risk hedging strategy, your funding cost forecasts, and your earnings guidance to investors.
How to Estimate Deposit Betas: The Core Methods
Method 1: The Historical Regression Approach
The classical approach: regress your average deposit rate on the risk-free benchmark (fed funds, SOFR, T-bill rate) over a historical period.
Formula:
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Deposit Rate(t) = Alpha + Beta × Market Rate(t) + Error
Where:
- Deposit Rate(t)
= Your bank's average deposit rate in period t (monthly or quarterly)
- Market Rate(t)
= The fed funds rate, 3-month SOFR, or relevant benchmark
- Beta
= The elasticity you're solving for
- Alpha
= The intercept (your deposit cost at zero market rates, often negative, reflecting credit value and liquidity convenience)
Example: JPMorgan's consumer deposit rate data from 2015–2021 (calm period):
- When fed funds was 2.5%, JPM paid ~0.80% on savings
- When fed funds was 0.25%, JPM paid ~0.15% on savings
- Over a regression of 80 quarters of data (2015–2019), the fitted beta was 0.32
This regression-based beta of 0.32 held reasonably through 2021. But when rates rose sharply in 2022–2023, the beta shifted to 0.65+. A simple regression of 2022–2023 data showed JPM's deposit rates rising 65 bps for every 100 bps of fed funds increase.
Strengths: Simple to compute, based on observable data, captures historical relationship.
Weaknesses: Assumes the past relationship holds in the future. Misses structural breaks. During regime shifts (2022 rate cycle), historical betas are misleading. Also sensitive to the chosen period: a regression from 2015–2021 (low-rate period) looks very different from 2022–2024 (high-rate period).
Method 2: Deposit Product-Level Betas
Instead of one bank-level beta, estimate betas by product:
- Non-interest checking: ~5–15% beta (very sticky, low competition)
- Savings/Money Market: ~50–80% beta (price-sensitive, moderate switching costs)
- Retail CDs: ~70–90% beta (mature product, highly competitive)
- Commercial deposits (DDA): ~60–85% beta (rate-sensitive, tight relationship leverage)
- Institutional deposits: ~95–100% beta (essentially trades at market rate)
Your bank-level beta is then the weighted average of product betas, weighted by product balance:
Bank Beta = Sum(Product Beta × Product Weight)
Example: BankX deposits are 30% NIB checking (5% beta), 40% savings (70% beta), 20% CDs (85% beta), 10% commercial (80% beta):
BankX Beta = (0.05 × 0.30) + (0.70 × 0.40) + (0.85 × 0.20) + (0.80 × 0.10) = 0.015 + 0.28 + 0.17 + 0.08 = 0.555
This framework is more robust than a single bank-level regression because it acknowledges that different products compete in different markets. It also lets you stress-test: "If mix shifts to 50% savings (higher beta), what's our all-in cost?"
Strength: Accounts for product heterogeneity and allows scenario analysis.
Weakness: Requires detailed product-level data. Small and medium-sized banks may not segment deposits finely enough. Betas by product are also unstable across rate regimes.
Method 3: Opportunity-Cost-Adjusted Betas (Real-time Tracking)
The most sophisticated approach: model deposit betas as a function of the opportunity cost gap rather than absolute market rates.
Opportunity Cost Gap = Risk-Free Rate Available to Depositor - Rate Paid by Your Bank
When the gap is small (e.g., 50 bps), deposits are sticky; you don't need to raise rates much. When the gap is large (e.g., 300+ bps), you must raise rates aggressively or lose deposits.
Formula:
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Deposit Rate Change = Base Repricing + Elasticity × Opportunity Cost Gap
Where:
- Base Repricing = Structural repricing (e.g., you always raise 30 bps of the 100 bps fed move)
- Elasticity = How much you raise rates beyond base repricing per 100 bps of opportunity cost gap
Example: Your bank's data shows:
- When opportunity cost gap = 50 bps, you raise deposit rates by 20 bps of a 100 bps fed move (base repricing of 20%)
- When gap = 200 bps, you raise 55 bps (base 20% + 35% from elasticity)
- When gap = 400 bps, you raise 75 bps (base 20% + 55% from elasticity)
This non-linear model captures the behavioral reality: you have pricing power when alternatives are weak, and you're forced into competitive rate wars when alternatives are strong.
Practical example from 2022–2023: During Q4 2022, regional banks faced a deposit crisis because opportunity cost gaps hit 300–400 bps. The model predicted deposit betas of 70–85%. Actual deposit outflows and repricing confirmed those predictions. Meanwhile, big banks with more stable deposit bases and lower sensitivity to basis risk showed betas of 50–65%, matching opportunity-cost-adjusted expectations.
Method 4: Peer-Benchmarking and Comparables
When your own data is sparse or unstable, use peer data. Look at competitors' deposit costs in their 10-K filings:
Average Deposit Cost = (Interest Paid on Deposits / Average Deposit Balance) × 100
Most banks disclose this. You can compare your beta to similarly-sized competitors. If JPMorgan (large, franchise strong) shows a beta of 0.50 and you show 0.75 on the same market moves, it signals either:
- Your deposits are less sticky (depositors more price-sensitive)
- Your competitive position is weaker
- You're losing market share to rate-setting competitors
Using peer averages is useful for sanity-checking your internal estimate.
Tracking Betas Across Rate Cycles
The 2022–2024 cycle taught us: deposit betas are regime-dependent. A historical beta from 2015–2021 is useless in a 400+ bps rate cycle.
Here's how to track betas in real time:
Monthly deposit tracking:
1. Calculate your 3-month rolling-average deposit rate (Fed reports this; you can also track internally)
2. Calculate the 3-month rolling-average fed funds effective rate
3. Regress the last 12–18 months of deposit rate changes on fed funds changes
4. Compare the fitted beta to your model beta
5. If actual beta > model beta by >15%, investigate
Example: Your model assumes a 0.50 beta for savings deposits. Month 10 of a 12-month regression, you observe:
- Fed funds rose 50 bps (month-over-month)
- Your savings rate rose 40 bps
- Your 12-month rolling beta = 0.62
This 12 bps excess repricing suggests faster-than-modeled competitive pressure. You might raise your beta assumption to 0.60 for the next quarter's funding forecast.
Real Examples: How Betas Failed (and Why)
SVB's Beta Mistake
SVB's 10-K filing (2022) disclosed that their deposit beta assumption was 0.25 for a rising rate environment. This came from 2015–2021 historical data. When the Fed raised rates 400+ bps in 2022–2023, SVB's deposits repriced at roughly 0.90+ (nearly dollar-for-dollar). The bank's actual deposit cost (interest paid / average deposits) rose from 0.35% to 2.80%—a 245 bps increase on a 400+ bps fed move.
Why did this happen? SVB held a high concentration of venture capital and tech-related deposits. These depositors are young, tech-savvy, hyper-rate-sensitive, and not sticky. When T-bills offered 5%, they withdrew. SVB had no deposit-pricing discipline because it had never competed in a high-rate environment. The bank's management learned too late that beta assumptions must account for depositor composition and competitive context.
Huntington's Beta Shock
Huntington Bancshares, a regional bank with $160B in deposits, built 2022 guidance assuming a 0.45 deposit beta. Q3 2022, actual beta came in at 0.72. By Q4, it was 0.78. In 10-K disclosures, Huntington noted that their deposit costs rose 135 bps in 2022 (on a ~275 bps fed move), implying an actual beta of 0.49—within guidance.
But this number masked intra-period volatility. In October–November 2022 alone, betas spiked to 0.80+. This happened because competitive pressure from other banks and money market funds intensified. Huntington had to raise rates faster to retain mid-sized commercial deposits. The monthly beta volatility exceeded quarterly beta by a factor of 2.
JPMorgan's Franchise Effect
JPMorgan, the largest U.S. bank, showed betas of 0.45–0.55 throughout the 2022–2023 cycle, despite a 400+ bps rate move. Why? Their deposit franchise includes:
- 50M+ retail customers with checking/savings linkage
- 10M+ credit cardholders (high switching cost)
- 4M+ investment management customers (sticky wealth management deposits)
- Dominant commercial banking presence in corporate payroll
These relationships are difficult to sever. Even when JPM paid 1.5% and a money market fund paid 4.5%, many depositors stayed because moving a payroll setup, credit products, and investment accounts was friction-intensive. JPM's deposit beta of 0.50 reflected this lower sensitivity. At the same time, JPM's deposits actually grew 1% in 2023, while smaller competitors lost 5–8%.
Forecasting Betas in Your Model
How do you forecast deposit betas for a 2024–2026 outlook? Here's a framework:
1. Start with current market betas (last 12 months of actual data).
2. Adjust for depositor composition (your book mix vs. peer mix).
3. Adjust for franchise strength (link betas to ROA, ROE, stock price relative to peer average).
4. Adjust for rate regime (in high-rate environments, assume higher betas; in low-rate environments, assume lower betas).
5. Build scenarios:
- Base case: Rates stable or declining slowly. Beta mean-reverts to 0.50.
- Stress case: Rates rise further. Beta stays at 0.70+.
- Upside case: Rates fall sharply, depositors flock back, beta falls to 0.35.
6. Sensitivity test: For every 50 bps of beta misestimation on $100B deposits, simulate earnings impact ($50M × (100 bps / 100)).
Key Takeaways
1. Use multiple methods: Don't rely on a single historical regression. Combine product-level analysis, opportunity-cost adjustment, and peer benchmarking.
2. Update monthly: Track deposit repricing in real time. Don't wait for quarterly earnings to discover your beta assumption was wrong.
3. Adjust for regime: Betas are unstable. Your 2020 beta is not your 2023 beta. Build conditional forecasts that vary by rate environment.
4. Stress for outliers: Some quarters will show wild beta spikes (200+ bps opportunity cost gap, competitive crisis). Stress your earnings and liquidity for these scenarios.
5. Franchise matters: Banks with strong deposit franchises (JPM, BofA) can sustain 0.45–0.50 betas. Weak franchises (SVB, many regionals) face 0.75+ betas. Understand where you sit and why.