Automated Risk Management Mechanisms in DeFi Lending Protocols: A Crosschain Comparative Analysis of Aave and Compound

Paper: arXiv 2506.12855

Authors: Erum Iftikhar, Wei Wei, John Cartlidge

Abstract

Blockchain-based decentralised lending is a rapidly growing and evolving alternative to traditional lending, but it poses new risks. To mitigate these risks, lending protocols have integrated automated risk management tools into their smart contracts. However, the effectiveness of the latest risk management features introduced in the most recent versions of these lending protocols is understudied. To close this gap, we use a panel regression fixed effects model to empirically analyse the cross-version (v2 and v3) and cross-chain (L1 and L2) effectiveness of liquidation mechanisms, measured through TVL and total revenue as proxies for performance of the two most popular lending protocols, Aave and Compound, during the period Jan 2021 to Dec 2024. Our analysis reveals that liquidation events in v3 of both protocols lead to an increase in total value locked and total revenue, with stronger impact on the L2 blockchain compared to L1. In contrast, liquidations in v2 have an insignificant impact, which indicates that the most recent v3 protocols have better risk management than the earlier v2 protocols. We also show that L1 blockchains are the preferred choice among large investors for their robust liquidity and ecosystem depth, while L2 blockchains are more popular among retail investors for their lower fees and faster execution.

Complexity vs Empirical Score

• Math Complexity: 3.0/10
• Empirical Rigor: 7.5/10
• Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: This paper presents a strong empirical analysis comparing protocol versions (v2 vs v3) and blockchain layers (L1 vs L2) in DeFi lending using panel fixed-effects regression. While the mathematical modeling is straightforward statistical econometrics rather than advanced quantitative math, the empirical design is rigorous, highly relevant, and backed by fully open code and data.

Research Flowchart

  flowchart TD
    A["Research Goal: Evaluate DeFi Risk Management<br/>(Aave & Compound: v2 vs v3, L1 vs L2)"] --> B["Data Collection (Jan 2021 - Dec 2024)<br/>TVL, Revenue, & Liquidation Events"]
    B --> C["Panel Regression Fixed Effects Model"]
    C --> D{"Cross-Version & Cross-Chain Analysis"}
    D --> E["Finding 1: v3 Liquidations Significantly<br/>Boost TVL & Revenue (v2 Impact Insignificant)"]
    D --> F["Finding 2: L2 Shows Stronger Risk Mitigation Effect;<br/>L1 Favored by Large Investors vs L2 for Retail"]