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BlockAnalitica proposes an automated solution to optimize Interest Rate Models (IRMs) in DeFi lending markets, involving a controller that adjusts borrow rates based on market utilization and equilibrium changes. While potential collaboration and chain adaptability are discussed, concerns about potential attack vectors and tradeoffs, such as Just-In-Time liquidity attacks, suggest further research is needed before implementation.
The discussion initiated by BlockAnalitica revolves around the limitations of the current DeFi lending markets and the proposal of a solution to optimize Interest Rate Models (IRMs). The proposed solution involves a controller that adjusts borrow rates to target market utilization around the optimal level, adapting to changes in equilibrium borrow rates without requiring active governance1. The controller's parameters, including update frequency, max/min target utilization, over/underutilization adjustment, and rate floor, are discussed in detail1.
BlockAnalitica also provides a comprehensive example of how the controller works and a mathematical explanation of how to calculate the supply rate at the min and max target utilization ratios1. The proposed mechanism involves granting ownership or permissions over the relevant interest rate model(s) to a controller, which would be owned by the protocol governance1. The controller would be responsible for triggering updates to the interest rate model based on the realized supply rate1.
The discussion also includes the potential for collaboration with TylerEther's rate controller, Adrastia Prudentia, and the use of geometric-mean time-weighted average supply rate oracles2. Monet-supply and TylerEther discuss the importance of minimizing gas-intensive operations and the potential of the proposed rate automation mechanism to limit gas costs 3,4,5.
However, Gauntlet highlights potential tradeoffs of automated IR mechanisms, including the introduction of potential attack vectors similar to Just-In-Time (JIT) liquidity attacks for Uniswap7. They suggest that reducing the reserve factor below certain thresholds could negate attacker profitability and propose considering supply and demand elasticities for IR parameter optimization7.
In conclusion, the discussion presents a detailed proposal for optimizing IRMs in DeFi lending markets, with potential for collaboration and adaptation to different chains. However, potential risks and tradeoffs are also highlighted, suggesting the need for further research and consideration before implementation.
Posted 5 months ago
Last reply 4 months ago
Summary updated 2 months ago
Last updated 09/12 13:52