[Paper Review] An Empirical Study of DeFi Liquidations: Incentives, Risks, and Instabilities

Kaihua Qin, Liyi Zhou+3 more|arXiv (Cornell University)|Jun 11, 2021

FinTech, Crowdfunding, Digital Finance18 citations

TL;DR

This paper presents the first empirical study of DeFi liquidation mechanisms across Aave, Compound, MakerDAO, and dYdX, analyzing incentives, risks, and systemic instabilities. It proposes an optimal fixed-spread liquidation strategy that increases liquidator profits by 1.36%—equivalent to $53.96K in one case—while exacerbating borrower losses due to excessive collateral liquidation at discounted rates.

ABSTRACT

Financial speculators often seek to increase their potential gains with leverage. Debt is a popular form of leverage, and with over 39.88B USD of total value locked (TVL), the Decentralized Finance (DeFi) lending markets are thriving. Debts, however, entail the risks of liquidation, the process of selling the debt collateral at a discount to liquidators. Nevertheless, few quantitative insights are known about the existing liquidation mechanisms. In this paper, to the best of our knowledge, we are the first to study the breadth of the borrowing and lending markets of the Ethereum DeFi ecosystem. We focus on Aave, Compound, MakerDAO, and dYdX, which collectively represent over 85% of the lending market on Ethereum. Given extensive liquidation data measurements and insights, we systematize the prevalent liquidation mechanisms and are the first to provide a methodology to compare them objectively. We find that the existing liquidation designs well incentivize liquidators but sell excessive amounts of discounted collateral at the borrowers' expenses. We measure various risks that liquidation participants are exposed to and quantify the instabilities of existing lending protocols. Moreover, we propose an optimal strategy that allows liquidators to increase their liquidation profit, which may aggravate the loss of borrowers.

Motivation & Objective

• To provide the first longitudinal, data-driven analysis of DeFi lending protocols’ liquidation mechanisms across Aave, Compound, MakerDAO, and dYdX.
• To quantify the risks and instabilities faced by liquidators and borrowers in existing DeFi liquidation systems.
• To develop a methodology for objectively comparing liquidation mechanisms based on incentives for borrowers versus liquidators.
• To propose and validate an optimal fixed-spread liquidation strategy that increases liquidator profits by leveraging close factor limitations across two transactions.

Proposed method

• The study analyzes on-chain data from 28,138 liquidation events across four major DeFi protocols over a 2-year period, covering total value locked (TVL) of $39.88B.
• It classifies liquidation price movements into seven categories (e.g., Rise, Fall, Rise-Fall) to assess timing risks and profitability.
• The authors derive a mathematical condition (Equation 16) to determine when fixed-spread liquidations can improve a borrowing position’s health factor, showing it’s only possible under specific collateral-to-debt ratios.
• An optimal liquidation strategy is proposed that uses two consecutive liquidations to bypass the close factor limit, maximizing profit while increasing borrower losses.
• The strategy is validated using real blockchain data from a past transaction, demonstrating a $53.96K profit increase (1.36%)
• The study uses statistical analysis to measure transaction fee sensitivity, bad debt accumulation, and price volatility exposure across protocols.

Experimental results

Research questions

• RQ1 How do existing DeFi liquidation mechanisms incentivize liquidators, and what are their systemic risks and instabilities?
• RQ2 To what extent do fixed-spread mechanisms favor liquidators over borrowers, and how much collateral is liquidated beyond what is necessary?
• RQ3 Can a liquidation strategy be designed to increase liquidator profits while remaining within protocol constraints?
• RQ4 What is the impact of price volatility and timing on liquidation profitability, especially in volatile price movement patterns?
• RQ5 How do different protocols (Aave, Compound, MakerDAO, dYdX) compare in terms of liquidation frequency, fee burden, and risk exposure?

Key findings

• The total liquidation proceeds across the four protocols amounted to $807.46M over two years, with 2,8138 liquidation events and 2,011 unique liquidator addresses.
• 73.97% of liquidations incurred above-average transaction fees, indicating high competition among liquidators.
• 641 auction-based liquidations were unprofitable for liquidators, suggesting risk in auction mechanisms.
• Aave V2 accumulated up to $87.4K in bad debts by April 2021, indicating positions that borrowers do not liquidate due to lack of incentive.
• A 43% drop in ETH price could trigger $1.07B in liquidatable collateral on MakerDAO, highlighting systemic vulnerability during market stress.
• The proposed optimal fixed-spread strategy increased liquidation profit by $53.96K (1.36%) in a real-world case, validating its effectiveness and potential to worsen borrower losses.