Dynamic Interest Rate Models (e.g., Jump Rate) vs Linear Interest Rate Models

Market-Responsive Rates: Algorithms like Aave's Jump Rate or Compound's kinked model adjust rates based on real-time utilization (e.g., >80% utilization triggers steep rate increases). This dynamically manages liquidity risk and optimizes capital allocation.

Best for: Lending protocols (Aave, Compound) in volatile markets where supply/demand shifts rapidly. Ensures protocol solvency by incentivizing rebalancing.

Incentivizes Equilibrium: Sharp rate hikes at high utilization (e.g., from 5% to 40% APY) aggressively attract suppliers and deter new borrowers, preventing liquidity crunches.

Best for: Large-scale money markets where protecting against bank runs and maintaining reserve health is a top priority. The model actively defends the protocol.

Transparent Calculation: Rates change at a constant slope relative to utilization (e.g., 0-100% utilization maps to 2-20% APY). Users can precisely calculate costs/returns at any utilization level.

Best for: Stablecoin pools (like early MakerDAO SFs) or predictable environments where user experience and simple integration are critical. Easy to audit and explain.

Computational Simplicity: Requires fewer on-chain operations (basic arithmetic) compared to dynamic piecewise functions. This reduces gas costs for rate updates and smart contract complexity.

Best for: Newer protocols or sidechain/L2 deployments where minimizing overhead and launch speed is essential. Also suits fixed-term lending products.

• Volatile, Deep Markets: Major lending/borrowing platforms (ETH, wBTC markets).
• Capital Efficiency Priority: Maximizing yield for suppliers when demand is high.
• Risk-Off Design: Non-negotiable need to protect reserves at all costs.

• Stable, Niche Assets: Long-tail assets with predictable flow.
• Developer Experience: Easier integration for wallets and front-ends.
• Gas-Sensitive Environments: Rollups or chains where every computation is costly.

Aggressive rate curve: Interest rates increase exponentially past a target utilization threshold (e.g., 80-90%). This creates a powerful incentive for borrowers to repay and lenders to supply, rapidly rebalancing the pool. Protocols like Compound v2 and Aave V2 use this to maintain liquidity during volatile market swings, protecting against bad debt.

Designed for tail-risk events: The "jump" in rates acts as a circuit breaker, making borrowing prohibitively expensive when liquidity is critically low. This protects the protocol's solvency by discouraging further leverage and attracting emergency capital. Essential for volatile assets like memecoins or during liquidation cascades.

Unpredictable borrowing costs: Rates can spike suddenly, catching borrowers off-guard and leading to unexpected liquidations. This complexity requires sophisticated monitoring tools (e.g., Gauntlet, Chaos Labs simulations) and deters less sophisticated users. Not ideal for protocols targeting stable, predictable DeFi primitives.

Can overshoot equilibrium: The sharp rate increase may drive utilization too far below the optimal threshold, leaving capital underutilized and reducing overall lender yields. Requires constant parameter tuning by DAOs or keepers, adding governance overhead compared to a set-and-forget linear model.

Transparent cost structure: Interest rates change at a constant slope relative to utilization. Borrowers can accurately forecast costs, and integration is straightforward for other protocols (e.g., money markets, yield aggregators). Used by early iterations of MakerDAO and many fixed-rate protocol foundations for stability.

Reduced parameter risk: With fewer knobs to turn (typically just a slope multiplier), there's less chance of a governance misconfiguration causing a protocol failure. This is advantageous for new protocols or those with less mature DAO structures that want to minimize operational complexity.

Ineffective during liquidity crises: A linear increase may not be steep enough to incentivize rapid rebalancing when utilization approaches 100%. This can lead to borrower queues, failed liquidations, and increased insolvency risk, as seen in early Iron Bank incidents during market crashes.

Fails to signal urgency: The model provides a weak economic signal for both lenders and borrowers when the pool becomes imbalanced. Capital may flow in too slowly during high demand, leading to missed yield opportunities for lenders and stifled growth for the protocol ecosystem.

Simple, deterministic pricing: Interest rates change at a constant, pre-defined slope (e.g., 2% per 10% utilization). This provides transparent and calculable costs for users and integrators. This matters for stable, long-tail asset pools where user experience and predictable borrowing costs are prioritized over perfect capital efficiency.

Lower computational overhead: The calculation is a simple rate = k * utilization. This results in cheaper transaction fees for interest accrual and rate updates. This matters for high-frequency DeFi operations on L2s or for protocols like Aave V2 on Ethereum Mainnet, where every gas unit counts for economic viability.

Aggressive supply incentives at high utilization: Models like Compound's Jump Rate introduce a kink point (e.g., 90% utilization) where rates jump sharply. This strongly incentivizes suppliers to deposit and borrowers to repay, protecting liquidity. This matters for volatile or highly demanded assets (e.g., ETH, stables) to prevent pool depletion and bad debt.

Dynamic defense against liquidity crises: The non-linear rate spike acts as a circuit breaker, making it prohibitively expensive to borrow the last portion of reserves. This directly mitigates the risk of a liquidity crunch. This matters for money market protocols with high TVL (e.g., Compound, Euler pre-hack) where systemic risk management is the top priority.