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Daniele Maria Di Nosse, Fabrizio Lillo · 2026-06-22
The paper builds a detailed simulation (agent-based model) of a Uniswap v3-style liquidity pool trading against a volatile reference market, including realistic blockchain frictions like block timing, mempool latency, and various trader types (arbitrageurs, MEV searchers, smart routers, active LPs). It tests dynamic fee rules that adjust based on volatility and order-flow toxicity to compensate liquidity providers for adverse-selection losses known as Loss-Versus-Rebalancing (LVR). Simulations suggest dynamic fees mainly help by raising fee income during stale-price periods, compensating for LVR rather than reducing the LVR itself.
Why it matters: For anyone providing liquidity in concentrated-liquidity AMMs, the work highlights that adverse selection (LVR) is a real cost and that volatility- and toxicity-linked dynamic fees may improve net hedged returns. It offers a framework for thinking about when LP profitability turns positive, though results come from simulation rather than live pools.
⚠ Findings come entirely from an agent-based simulation with assumed dynamics (Heston volatility, stylized agents), so they may not hold in live on-chain markets.
Automated Market Makers based on concentrated liquidity, such as Uniswap v3, significantly improve capital efficiency but expose Liquidity Providers (LPs) to adverse selection costs, formalized as Loss-Versus-Rebalancing (LVR). While theoretical literature quantifies these costs, the interplay between realistic blockchain microstructure and endogenous pricing mechanisms remains under-explored. This paper develops a granular Agent-Based Model of a Uniswap v3 pool interacting with a stochastic reference market governed by Heston volatility dynamics. The framework incorporates discrete block propagation, mempool latency, and a heterogeneous population of agents, including latency-sensitive arbitrageurs, smart routers, Maximal Extractable Value searchers, and active LPs benchmarked against a frictionless rebalancing strategy. We propose and evaluate dynamic fee schedules driven by volatility and order-flow toxicity proxies intended to compensate LPs for adverse-selection losses. Our simulations investigate the conditions under which LPs can achieve positive hedged Profit and Loss (fees minus LVR). The analysis suggests that dynamic fee adjustments can improve hedged LP profitability mainly by increasing fee income in states associated with stale-price risk. Depending on the configuration, these rules may also affect realized LVR, but the current aggregate results support compensation for LVR more directly than a reduction of LVR itself.
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AI summary generated from the paper’s public abstract via arXiv; it may miss nuance — read the source before relying on it. Thank you to arXiv for its open-access interoperability; StockTools is not affiliated with arXiv, and all rights remain with the authors. Educational only, not financial advice.