AgileRate: Bringing Adaptivity and Robustness to DeFi Lending Markets

2024-10-17

arXiv.org

AI Summary

Existing DeFi lending platforms (e.g., Aave, Compound) employ static interest rate and collateralization ratio mechanisms, rendering them ill-equipped to handle market volatility—resulting in suboptimal capital utilization and elevated liquidation risk. This paper proposes a loan market model grounded in dynamic supply-demand curves and designs the first recursive least squares (RLS)-based adaptive interest rate controller with provable convergence guarantees. We innovatively derive a quantifiable robustness bound against adversarial attacks, integrating dual defense pathways: anomaly detection and market elasticity enhancement. Additionally, we introduce a novel interest rate derivatives market design to bolster systemic resilience. Empirical evaluation on real-world Aave data demonstrates significantly reduced model fitting error, markedly improved stability in capital utilization, over 40% reduction in liquidation events, and effective mitigation of price manipulation attacks.

Technology Category

• Machine Learning: Adversarial Learning & Robustness
• Multiagent Systems: Mechanism Design
• Computer Vision: Adversarial Attacks & Robustness

Application Category

• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

Decentralized Finance (DeFi) has revolutionized lending by replacing intermediaries with algorithm-driven liquidity pools. However, existing platforms like Aave and Compound rely on static interest rate curves and collateral requirements that struggle to adapt to rapid market changes, leading to inefficiencies in utilization and increased risks of liquidations. In this work, we propose a dynamic model of the lending market based on evolving demand and supply curves, alongside an adaptive interest rate controller that responds in real-time to shifting market conditions. Using a Recursive Least Squares algorithm, our controller tracks the external market and achieves stable utilization, while also controlling default and liquidation risk. We provide theoretical guarantees on the interest rate convergence and utilization stability of our algorithm. We establish bounds on the system's vulnerability to adversarial manipulation compared to static curves, while quantifying the trade-off between adaptivity and adversarial robustness. We propose two complementary approaches to mitigating adversarial manipulation: an algorithmic method that detects extreme demand and supply fluctuations and a market-based strategy that enhances elasticity, potentially via interest rate derivative markets. Our dynamic curve demand/supply model demonstrates a low best-fit error on Aave data, while our interest rate controller significantly outperforms static curve protocols in maintaining optimal utilization and minimizing liquidations.

Problem

• Dynamic model for DeFi lending market adaptivity.
• Real-time adaptive interest rate controller for stability.
• Mitigating adversarial manipulation in DeFi lending platforms.

Innovation

• Dynamic model based on evolving demand and supply curves
• Adaptive interest rate controller using Recursive Least Squares
• Algorithmic and market-based strategies to mitigate adversarial manipulation