Optimal Fees for Geometric Mean Market Makers

TL;DR

This paper develops a framework to determine optimal trading fees for Geometric Mean Market Makers (G3Ms) in decentralized exchanges, balancing price accuracy and LP incentives, using diffusion process modeling.

Contribution

It introduces a novel method for selecting optimal fees in CFMMs, specifically G3Ms, based on diffusion process analysis to maximize LP value.

Findings

LPs prefer G3Ms with minimal fees under certain utility assumptions

The framework balances price accuracy with LP incentives

Optimal fees depend on the underlying diffusion process

Abstract

Constant Function Market Makers (CFMMs) are a family of automated market makers that enable censorship-resistant decentralized exchange on public blockchains. Arbitrage trades have been shown to align the prices reported by CFMMs with those of external markets. These trades impose costs on Liquidity Providers (LPs) who supply reserves to CFMMs. Trading fees have been proposed as a mechanism for compensating LPs for arbitrage losses. However, large fees reduce the accuracy of the prices reported by CFMMs and can cause reserves to deviate from desirable asset compositions. CFMM designers are therefore faced with the problem of how to optimally select fees to attract liquidity. We develop a framework for determining the value to LPs of supplying liquidity to a CFMM with fees when the underlying process follows a general diffusion. Focusing on a popular class of CFMMs which we call Geometric Mean Market Makers (G3Ms), our approach also allows one to select optimal fees for maximizing LP value. We illustrate our methodology by showing that an LP with mean-variance utility will prefer a G3M over all alternative trading strategies as fees approach zero.