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动态AMM费用与流动性提供者收益保护

文章 arXiv papers · 作者: Steven Campbell et al.
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总结

本研究考察自动做市商费用如何影响被动流动性提供者的盈利能力，研究中的AMM与中心化交易所竞争。在其简化模型中，交易者将订单导向价格较优的一方，而套利者则针对价格差异进行交易。这些机制会使流动性提供者遭受逆向选择损失，静态费用可能无法弥补这些损失。

研究利用大规模模拟和真实市场数据校准，分析LP利润及最优费用与波动率、交易量等条件之间的关系。费用需要在吸引订单流、产生收入和弥补套利损失之间权衡。在一般情况下，研究称最优费用与中心化交易所交易成本相比具有竞争力，且相对稳定；波动率极高时，较高费用可以保护提供者免受更大损失。所提出的阈值式动态费用安排是基于模型得出的结果，其实用性取决于模型和校准是否能代表实际市场行为。

核心观点

  • 模型纳入AMM与中心化交易所之间的竞争，并包括交易者订单路由和套利者利用价格差。
  • 如果费用不足以弥补损失，逆向选择可能使被动提供流动性无利可图。
  • 最优费用需要在吸引交易量、产生收入和减少套利损失之间权衡。
  • 研究发现，正常情况下费用相对稳定，但波动率极高时应提高费用。
  • 所提出的阈值式费用安排得到模拟和市场数据校准的支持。

标签

加密资产去中心化金融做市波动率风险管理
全文
# Optimal Fees for Liquidity Provision in Automated Market Makers


# Optimal Fees for Liquidity Provision in Automated Market Makers









Passive liquidity providers (LPs) in automated market makers (AMMs) face losses due to adverse selection (LVR), which static trading fees often fail to offset in practice. We study the key determinants of LP profitability in a dynamic reduced-form model where an AMM operates in parallel with a centralized exchange (CEX), traders route their orders optimally to the venue offering the better price, and arbitrageurs exploit price discrepancies. Using large-scale simulations and real market data, we analyze how LP profits vary with market conditions such as volatility and trading volume, and characterize the optimal AMM fee as a function of these conditions. We highlight the mechanisms driving these relationships through extensive comparative statics, and confirm the model's relevance through market data calibration. A key trade-off emerges: fees must be low enough to attract volume, yet high enough to earn sufficient revenues and mitigate arbitrage losses. We find that under normal market conditions, the optimal AMM fee is competitive with the trading cost on the CEX and remarkably stable, whereas in periods of very high volatility, a high fee protects passive LPs from severe losses. These findings suggest that a threshold-type dynamic fee schedule is both robust enough to market conditions and improves LP outcomes.

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。