Dynamic vs. Static Liquidation Penalties

Specific advantage: Penalty adjusts based on market volatility and collateral health (e.g., Aave's Liquidation Bonus). This matters for protocol stability during black swan events, as it dynamically incentivizes liquidators to clear underwater positions faster, protecting the system's solvency.

Specific advantage: Allows for lower collateralization ratios (e.g., 110% on MakerDAO vs. 150%+ static models). This matters for borrowers seeking leverage, as it maximizes the utility of locked capital, but requires robust oracle feeds and active liquidator networks.

Specific advantage: Fixed fee (e.g., 13% on Compound, 10% on Liquity). This matters for risk modeling and user experience, as borrowers and liquidators can precisely calculate costs and incentives, reducing uncertainty in standard market conditions.

Specific advantage: No dependency on complex volatility oracles. This matters for newer L1/L2 deployments and forked protocols, as it reduces engineering overhead and attack surface, making the system easier to audit and deploy.

Specific advantage: Penalties scale with market volatility and position size, automatically increasing during stress (e.g., from 10% to 15%). This matters for highly volatile assets like memecoins or leveraged perpetuals, as seen in protocols like Aave V3 with its risk-adjusted parameters.

Specific advantage: Captures more value during liquidation cascades, directly boosting the protocol's treasury or token buybacks. This matters for protocols prioritizing sustainable revenue, providing a buffer against bad debt, similar to MakerDAO's surplus buffer mechanism.

Specific disadvantage: Introduces oracle latency risks and complex parameter tuning, making user cost calculations opaque. This matters for institutional users and auditors who require deterministic, predictable outcomes, a strength of simpler systems like Compound v2.

Specific disadvantage: Can lead to overly punitive liquidations during extreme volatility, alienating users and increasing systemic risk if penalties exceed collateral value. This matters for mainstream DeFi adoption, where user experience and fairness are critical.

Specific advantage: Fixed percentage (e.g., 13% in Liquity) is easy to model, audit, and communicate to users. This matters for protocols valuing extreme reliability and composability, as seen in Euler Finance's pre-hack design and many forked codebases.

Specific advantage: Users can precisely calculate worst-case costs, fostering trust. Liquidators have predictable profits, ensuring reliable keeper networks. This matters for building long-term, sticky user bases in lending/borrowing markets.

Specific disadvantage: Fixed rate may be insufficient to cover bad debt during black swan events, risking protocol insolvency. This matters for protocols with exotic or correlated collateral where static models can fail, unlike Maker's dynamic Stability Fee adjustments.

Specific disadvantage: Requires higher initial collateral ratios (e.g., 110% vs. 105%) to build safety buffers, locking up more user capital. This matters for competing on yields and leverage in crowded markets like Ethereum L2 lending.

Market-Responsive Risk Management: Penalties adjust based on collateral volatility and market depth (e.g., Aave's dynamic penalty model). This automatically increases the liquidation incentive during high volatility, protecting the protocol's solvency when it matters most. Ideal for permissionless pools with diverse, volatile assets like memecoins or LSTs.

Complexity and Predictability Cost: Introduces oracle dependency and complex parameter tuning. Users and bots cannot precisely forecast liquidation costs, complicating risk management strategies. This adds overhead for integrators (like DeFi aggregators) and can lead to unexpected, large penalties during flash crashes, as seen in some early Compound markets.

Simplicity and Composability: A fixed penalty (e.g., MakerDAO's 13% liquidation penalty) is transparent and easily calculable. This predictability is crucial for automated strategies, vaults (like Yearn), and derivative protocols that need guaranteed math. It reduces integration overhead and smart contract audit surface.

Inflexible to Market Shocks: A penalty that's too low may not incentivize liquidators during a black swan event, risking bad debt (as occurred with undercollateralized positions on early lending platforms). A penalty that's too high unnecessarily punishes users during normal market conditions, reducing capital efficiency.