发表机构
University of Florida; Stevens Institute of Technology（佛罗里达大学; 史蒂文斯理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出预言机参数化做市商（OP-AMMs），在严格收缩条件下证明报价在预言机价格与储备自给价格间插值，推导LVR分解，并发现OP-AMMs在提高资本效率的同时降低归一化LVR，但噪声或过时预言机会逆转收益，回测验证了权衡。
AI 中文摘要
本文介绍了预言机参数化的自动做市商（OP-AMMs），即其报价同时依赖于池储备和外部预言机价格的自动做市商。通过这样做，我们将信息无关的AMM框架扩展到诸如代币化证券等价格发现在链外发生的场景。在严格的预言机收缩条件下，我们证明了任何OP-AMM的报价在预言机价格和由池储备决定的隐式自给价格之间进行插值。然后，我们推导出一个一般的损失与再平衡（LVR）分解，将市场滞后带来的残余风险与预言机错误引起的损失分开。该分析进一步扩展到过时的、离散更新的预言机以及围绕预言机更新的三明治攻击。利用这一框架，我们找到了OP-AMMs同时提高局部资本效率并相对于信息无关的AMMs降低归一化LVR的条件。然而，足够嘈杂或过时的预言机可能逆转这些收益。我们提供了一个使用一秒SPY NBBO数据的反事实回测来展示这些权衡。特别是，我们绘制了预言机参数化的常数函数做市商（OP-CFMM）设计在典型预言机机制下的帕累托有效前沿。
英文摘要
This paper introduces oracle-parametrized automated market makers (OP-AMMs), i.e., automated market makers whose quoted price depends jointly on the pool reserves and an external oracle price. In doing so, we extend the information-agnostic AMM framework to settings, such as tokenized securities, for which price discovery occurs off-chain. Under a strict oracle-contraction condition, we show that the quoted price of any OP-AMM interpolates between the oracle price and an implicit autarkic price determined by the pool reserves. We then derive a general loss-versus-rebalancing (LVR) decomposition that separates the residual exposure to market lags from the losses induced by oracle errors. This analysis is further extended to stale, discrete-update oracles and to sandwich attacks around oracle updates. Using this framework, we find conditions under which OP-AMMs simultaneously increase local capital efficiency and reduce normalized LVR relative to information-agnostic AMMs. However, sufficiently noisy or stale oracles can reverse these gains. A counterfactual backtest using one-second SPY NBBO data is provided to demonstrate these trade-offs. In particular, we map the Pareto-efficient frontier of oracle-parametrized constant function market maker (OP-CFMM) designs across stylized oracle regimes.
Comments45 pages, 3 figures
