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衡量 Uniswap 主动与被动流动性提供者收益

文章 arXiv papers · 作者: Agathe Sadeghi et al.
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总结

本研究考察主动调整资金配置的流动性提供者与保持资金不动者之间的收益差异。研究提出两种估算被动表现的方法：一种识别短暂的流动性头寸，并将其贡献与兑换结果分离；另一种则对始终处于活跃价格区间内的边际提供者建模。研究将这些估算与不同版本 Uniswap 及多个区块链网络上的资金池整体结果进行比较。

研究发现，资金池平均值可能掩盖不同参与者承担逆向选择风险的显著差异。在流动性广泛分布的资金池中，被动表现估算值接近整体表现。集中流动性资金池中的被动提供者表现则有更大缺口，在以太坊上尤其明显；作者认为，更快地响应交易流可能有利于主动提供者。手续费较高的资金池通常更有利于被动流动性。两种估算方法的方向大多一致，为这一模式提供了支持。分析仅限于所研究的协议、资金池和区块链，报告的关联也不能说明差距产生的原因。结果还促使人们进一步考察手续费设计以及去中心化交易所质量的衡量方式。

核心观点

  • 资金池整体收益可能掩盖主动与被动流动性提供者之间的差异。
  • 两种互补的交易后价格变动分析方法利用交易和价格数据估算被动表现。
  • 在流动性广泛分布的资金池中，被动表现与整体结果相近。
  • 集中流动性资金池中的被动表现较差，且在以太坊上的差距更大。
  • 手续费较高的资金池往往更有利于被动提供者。
  • 跨方法比较支持这些发现的方向，但结果仍仅适用于所研究的情境。

标签

去中心化金融做市风险管理统计学回测
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# Not All LPs Are Equal: The Active-Passive Gap in Automated Market Maker Liquidity Provision


# Not All LPs Are Equal: The Active-Passive Gap in Automated Market Maker Liquidity Provision









Liquidity provision in automated market makers is typically analyzed at the pool level, implicitly assuming LP homogeneity. This aggregate view can hide how liquidity provision outcomes differ between LP strategies, particularly as concentrated liquidity AMM designs operating on high-performance blockchains allow liquidity to be actively repositioned around trades. We develop a markout-based framework to decompose Uniswap LP profitability into active and passive components using two complementary methods: a LIFO subtraction method that matches short-lived mint-burn positions and attributes swap-level markouts by liquidity share; and an infinitesimal LP benchmark that estimates the performance of a fully passive, always-in-range marginal LP directly from the AMM price path. We apply these methods to Uniswap v2, v3, and v4 pools on Ethereum, Arbitrum, and Base chains, and find passive profitability can materially differ from aggregate pool profitability.In Uniswap v2, with liquidity distributed evenly and active LP behavior nearly absent, the overall and passive markouts are almost the same. In contrast, concentrated-liquidity pools have a systematic active-passive gap: passive LPs tend to underperform aggregate pool-level measures. The gap is wider on Ethereum than on L2s, consistent with active liquidity provision being more useful when block times and ordering conditions allow LPs to react to incoming flow. In general, passive LPs perform better on higher fee pools. The LIFO and infinitesimal estimates are generally consistent directionally across most pools, providing evidence of the robustness of the decomposition. The results suggest that adverse selection in AMMs is not evenly distributed among LPs, with important implications for LP strategy, fee-tier design and measurement of DEX market quality.

在遵守原作品许可的前提下，附作者信息全文展示。 许可协议: abstract CC0

此摘要由 Stratmill 研究智能体根据原文撰写，并非原文副本。