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Liquidation incentives after execution costs

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A liquidation bonus compensates a liquidator for repaying debt and disposing of collateral. The headline bonus is not net profit.

Assume the liquidator repays a fixed dollar debt amount and receives collateral quoted at that amount plus a bonus. A discount on actual collateral sale can consume the bonus.

Add gas, failed-transaction costs and financing. Close-factor limits, oracle updates and competing liquidators affect whether the operation is available and profitable. This is an economics model; it does not submit transactions.

Apply it in a strategy

• Investigate whether liquidation incentives remain viable under thin collateral liquidity and high fees.
• Record the input timestamp, executable quantity, currency and horizon. Reconcile the result with a cash-flow or state table.
• Stress this failure condition: Oracle collateral value may exceed attainable sale value, and failed attempts can still incur costs.

Research deliverable

Build and explain a liquidation incentives after execution costs worksheet. Investigate whether liquidation incentives remain viable under thin collateral liquidity and high fees.

Evidence boundary: Synthetic arithmetic and scenarios illustrate mechanics. They are not historical returns, a paper replication, or evidence of an executable edge. Research sources and their access limitations are recorded at the end of this module.

Primary research and operational references · reviewed 12 September 2026

Further reading: Milionis et al. — Automated Market Making and Loss-Versus-Rebalancing ↗

arXiv manuscript v6, revised 20 August 2026; publication status beyond this record not checked · Review: Abstract-only review 2026-09-12. Markets/data: Continuous-time model and Uniswap v2 ETH–USDC illustration; empirical sample dates not verified. Interpretation: Separates inventory market exposure from fees and adverse selection against a rebalancing benchmark. Limits: Abstract-only access; no empirical magnitude is used here. Discrete block trading, fees and oracle changes alter a continuous model. No independent replication performed.

Further reading: Uniswap — V2 pricing ↗

Official protocol documentation, V2 · Review: Documentation inspected 2026-09-12. Markets/data: Constant-product pools; no historical sample. Interpretation: Operational reference for reserve-based swap arithmetic and external price checks. Limits: The lessons label simplified fee and execution assumptions; other pool versions require different formulas. No independent replication performed.

Further reading: Aave — V3 overview ↗

Official protocol documentation, V3 · Review: Overview inspected 2026-09-12. Markets/data: Lending protocol mechanics; no empirical sample. Interpretation: Starting point for collateral, debt and health-factor research. Limits: Parameters differ by network, asset and governance state. Examples are generic lending models, not copied live parameters. No independent replication performed.

Research sources, review dates and limitations

# Python 3.10+; standard library unless NumPy is imported below.
# Inputs and outputs use the units defined in this lesson. Synthetic teaching example.
def liquidation_economics(debt,bonus,slippage,gas):
    if min(debt,bonus,gas)<0 or not 0<=slippage<1: raise ValueError("Invalid liquidation inputs")
    return debt*(1+bonus)*(1-slippage)-debt-gas

print(liquidation_economics(1000,.05,.02,10))