Automated Market Making and Loss-Versus-Rebalancing
Overview
Paper Summary
This paper develops a Black-Scholes-inspired model to quantify "loss-versus-rebalancing" (LVR), the cost incurred by decentralized exchange liquidity providers due to price slippage from informed arbitrageurs. The model shows that AMM LPs systematically trade at worse-than-market prices, with LVR depending on asset volatility and marginal liquidity. Empirically validated on Uniswap v2 WETH-USDC, the study demonstrates that delta-hedging LP positions significantly reduces market risk, making LP returns primarily a bet on fees offsetting LVR.
Explain Like I'm Five
Imagine you run a lemonade stand where you always sell for a bit less than the big store. Smart kids will buy from you cheaply and sell to the big store for a quick buck. This paper shows how much money you lose this way, and how you can avoid losing money when the price of lemons jumps around.
Possible Conflicts of Interest
Tim Roughgarden is Head of Research at a16z Crypto, a venture capital firm with significant investments in automated market making protocols. This affiliation presents a potential conflict as his research directly pertains to the profitability and design of systems that a16z Crypto invests in, potentially influencing the firm's strategic decisions or benefiting its portfolio companies. Additionally, Jason Milionis received an unrestricted gift from Gnosis, Ltd., another crypto project, which could also be seen as a minor conflict.
Identified Limitations
Rating Explanation
The paper presents a novel and well-developed theoretical model for understanding AMM LP losses (LVR) that is also quantitatively realistic and empirically validated. Its insights into delta-hedging and the distinction from impermanent loss are significant contributions to DeFi research. The limitations regarding idealized model assumptions and empirical scope are acknowledged by the authors and are common in foundational theoretical work. The conflicts of interest are noted but do not fundamentally undermine the scientific rigor of the methodology itself.
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