协议费用变化对自动化做市商流动性供应的因果效应
Causal Effects of Protocol-Fee Changes on Liquidity Provision in Automated Market Makers
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中文总结 AI 辅助
研究协议费用变化对自动化做市商流动性供应的因果效应，利用Uniswap协议费用切换的实际变化，通过匹配重叠事件研究差异设计估计流动性供应响应，未检测到短期平均响应，界定了估计量。
中文摘要 AI 辅助
自动化做市商（AMM）费用规则通常根据流动性提供者（LP）的福利来评估，但该目标混淆了费用收入、逆向选择损失（损失与重新平衡，LVR）、路由响应和流动性供应。固定费用的Uniswap v3历史无法区分这些渠道或识别面向交易者的反事实动态费用规则。不过，实际与费用相关的变化确实存在：Uniswap协议费用切换以分层差异化强度降低了LP的收取率，而面向交易者的费用保持不变。我们使用预先指定的匹配重叠事件研究差异设计，估计了对收取率削减的流动性供应响应，即基于模拟器的费用控制器评估通常冻结的内核K_L。我们在活跃流动性或局部深度中未检测到短期平均响应；LP的参与和构成也未显示出响应。代币1交易量和原生费用收入未通过平行趋势检验，我们进行了描述性报告。渠道可接受性审计界定了估计量：LP方响应K_L基于设计，而面向交易者的动态费用保护是模型条件边界，而非第二个估计量。
英文摘要
Automated market maker (AMM) fee rules are often evaluated by liquidity-provider (LP) welfare, but that objective mixes fee revenue, adverse-selection loss (loss-versus-rebalancing, LVR), routing response, and liquidity supply. Fixed-fee Uniswap v3 history cannot separate these channels or identify counterfactual trader-facing dynamic-fee rules. Real fee-related variation nonetheless exists: the Uniswap protocol-fee switch cut LP take-rates with tier-differentiated intensity while leaving trader-facing fees unchanged. Using a pre-specified matched-overlap event-study difference-in-differences design, we estimate the liquidity-supply response to take-rate cuts, the kernel K_L that simulator-based fee-controller evaluations routinely freeze, while reconstructing treatment, event time, unit roles, and outcomes from public logs into a frozen, hash-checked panel before any estimate. We detect no large short-run average response in active liquidity or local depth; LP participation and composition, more precisely estimated, likewise show none, so the result is a non-detection at the design's resolution rather than a precise zero. Token-1 volume and native fee income fail the parallel-trends gate and are reported descriptively. A channel-admissibility audit delimits the estimand: the LP-side response K_L is design-based, while trader-facing dynamic-fee protection is a model-conditioned boundary, not a second estimand.
