[论文解读] A Myersonian Framework for Optimal Liquidity Provision in Automated Market Makers
本文提出了一种基于Myerson框架的自动做市商(AMMs)最优流动性提供机制,将流动性提供者(LPs)建模为利润最大化主体,其通过类似贝叶斯的推理方式更新信念。该研究通过虚拟价值刻画了激励相容的AMM,并表明最优需求曲线中的买卖价差源于不利选择与市场势力的相互作用,且在信息不对称性更高时价差扩大。
In decentralized finance ("DeFi"), automated market makers (AMMs) enable traders to programmatically exchange one asset for another. Such trades are enabled by the assets deposited by liquidity providers (LPs). The goal of this paper is to characterize and interpret the optimal (i.e., profit-maximizing) strategy of a monopolist liquidity provider, as a function of that LP's beliefs about asset prices and trader behavior. We introduce a general framework for reasoning about AMMs based on a Bayesian-like belief inference framework, where LPs maintain an asset price estimate. In this model, the market maker (i.e., LP) chooses a demand curve that specifies the quantity of a risky asset to be held at each dollar price. Traders arrive sequentially and submit a price bid that can be interpreted as their estimate of the risky asset price; the AMM responds to this submitted bid with an allocation of the risky asset to the trader, a payment that the trader must pay, and a revised internal estimate for the true asset price. We define an incentive-compatible (IC) AMM as one in which a trader's optimal strategy is to submit its true estimate of the asset price, and characterize the IC AMMs as those with downward-sloping demand curves and payments defined by a formula familiar from Myerson's optimal auction theory. We generalize Myerson's virtual values, and characterize the profit-maximizing IC AMM. The optimal demand curve generally has a jump that can be interpreted as a "bid-ask spread," which we show is caused by a combination of adverse selection risk (dominant when the degree of information asymmetry is large) and monopoly pricing (dominant when asymmetry is small). This work opens up new research directions into the study of automated exchange mechanisms from the lens of optimal auction theory and iterative belief inference, using tools of theoretical computer science in a novel way.
研究动机与目标
- 在不确定且信息不对称的条件下,建模垄断型流动性提供者的利润最大化策略。
- 形式化一种信念更新机制,使LP能够根据交易者报告的估值来优化其资产价格估计。
- 刻画激励相容的AMM,使得交易者如实报告其价格估计值是最优策略。
- 推导在噪声交易与知情交易并存时,能最大化LP利润的最优需求曲线。
- 分析不利选择风险与垄断定价之间的结构性权衡,以确定最优无交易区间(即买卖价差)的形成机制。
提出的方法
- 将流动性提供者建模为市场 maker,其选择需求曲线 g(p),将价格映射为资产持有量。
- 引入一种类似贝叶斯的信念更新机制,即LP在每次交易后根据交易者报告的出价更新其价格估计。
- 通过要求交易者如实报告其价格估计值来定义激励相容性,该条件在需求曲线向下倾斜且支付遵循Myerson风格公式时成立。
- 将Myerson的虚拟价值推广至包含信念更新与信息不对称的情境,定义了上、下虚拟价值函数 φu(s;λ) 与 φl(s;λ)。
- 将最优需求曲线建模为虚拟盈余最大化问题的解,最优机制在真实价格 p0 周围包含一个无交易区间。
- 通过根查找分析虚拟价值函数,刻画无交易区间如何随信息不对称性增加(即 λ 降低)而扩大。
实验结果
研究问题
- RQ1流动性提供者应如何最优设定其需求曲线,以在考虑交易者私有信息的前提下最大化利润?
- RQ2在何种条件下AMM应避免交易?无交易区间的大小由什么决定?
- RQ3不利选择与市场势力如何共同决定AMM中最优买卖价差的形态?
- RQ4信念更新在塑造最优机制中起什么作用?其与Myerson最优拍卖设计有何关联?
- RQ5当信息不对称程度(λ)从纯噪声交易到完全知情交易变化时,最优机制如何演变?
主要发现
- 仅当需求曲线向下倾斜且支付遵循基于虚拟价值的Myerson风格公式时,最优AMM机制才具备激励相容性。
- 最优需求曲线在真实价格 p0 周围包含一个无交易区间,可解释为买卖价差。
- 随着信息不对称性增加(即 λ 减小),无交易区间的大小也随之扩大,反映出不利选择风险上升。
- 当 λ = 1(纯噪声交易)时,无交易区间对应于纯粹的垄断定价,价差完全由市场势力决定。
- 当 λ = 0(交易者完全知情)时,最优解为完全不交易,因为上下虚拟价值函数均非正。
- 随着 λ 减小,上虚拟价值函数的根增大,下虚拟价值函数的根减小,导致无交易区间因信息不对称性提高而扩大。
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