Mitigating Adverse Selection in Concentrated Liquidity AMMs with Dynamic Fees: An Agent-Based Model Approach

2026-06-22

AI Summary

This study addresses the significant loss in liquidity value (LVR) experienced by liquidity providers (LPs) in concentrated liquidity automated market makers like Uniswap v3, which stems from adverse selection and is inadequately compensated by existing fee mechanisms. To tackle this challenge, the authors propose and evaluate a dynamic fee mechanism that integrates volatility and order flow toxicity metrics. For the first time, they develop a multi-agent simulation framework grounded in realistic on-chain microstructure, incorporating a Heston stochastic volatility market model, block propagation delays, and heterogeneous participant behaviors—including MEV searchers and smart routing strategies. Experimental results demonstrate that the proposed mechanism substantially increases LP fee revenue under price lag risk, enabling their hedged PnL to turn positive, thereby validating the efficacy of dynamically compensating for LVR rather than attempting its complete elimination.

Abstract

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.

Problem

• Adverse Selection
• Concentrated Liquidity AMMs
• Loss-Versus-Rebalancing
• Dynamic Fees
• Liquidity Providers

Innovation

• Dynamic Fees
• Adverse Selection
• Agent-Based Modeling
• Loss-Versus-Rebalancing (LVR)
• Concentrated Liquidity AMMs