Dynamic Fee for Reducing Impermanent Loss in Decentralized Exchanges

TL;DR

This paper introduces dynamic fee mechanisms for decentralized exchanges to effectively reduce impermanent loss, outperforming fixed fees through adaptive algorithms that leverage all available data for better market efficiency.

Contribution

It proposes novel adaptive fee algorithms, including oracle-based methods, to mitigate impermanent loss in DEXs, supported by a comprehensive simulation framework.

Findings

Adaptive algorithms outperform fixed fees in reducing IL

Dynamic fees maintain trading activity among uninformed users

Oracle-based strategies show potential to lower IL and increase LP profitability

Abstract

Decentralized exchanges (DEXs) are crucial to decentralized finance (DeFi) as they enable trading without intermediaries. However, they face challenges like impermanent loss (IL), where liquidity providers (LPs) see their assets' value change unfavorably within a liquidity pool compared to outside it. To tackle these issues, we propose dynamic fee mechanisms over traditional fixed-fee structures used in automated market makers (AMM). Our solution includes asymmetric fees via block-adaptive, deal-adaptive, and the "ideal but unattainable" oracle-based fee algorithm, utilizing all data available to arbitrageurs to mitigate IL. We developed a simulation-based framework to compare these fee algorithms systematically. This framework replicates trading on a DEX, considering both informed and uninformed users and a psychological relative loss factor. Results show that adaptive algorithms outperform fixed-fee baselines in reducing IL while maintaining trading activity among uninformed users. Additionally, insights from oracle-based performance underscore the potential of dynamic fee strategies to lower IL, boost LP profitability, and enhance overall market efficiency.