Dynamic Fee for Reducing Impermanent Loss in Decentralized Exchanges

2025-06-03

AI Summary

Liquidity providers (LPs) in decentralized exchanges (DEXs) suffer significant impermanent loss (IL) due to price volatility and symmetric fee structures, undermining capital efficiency and participation incentives. Method: This paper proposes a triple-adaptive dynamic fee mechanism—adjusting fees at the block, transaction, and oracle-feedback levels—featuring asymmetric pricing calibrated to arbitrageurs’ full-information strategies and incorporating a behavioral psychology–informed relative loss aversion factor to model LP risk preferences. The design integrates game-theoretic modeling, behavioral finance principles, on-chain data–driven algorithms, and an off-chain oracle–enabled closed-loop feedback system. Contribution/Results: Empirical evaluation demonstrates that the mechanism reduces IL by 37% and increases LP annualized returns by 22% compared to fixed-fee baselines, while preserving uninformed user trading activity and significantly enhancing market efficiency—without introducing measurable friction for non-arbitrage trades.

Technology Category

• Game Theory and Economic Paradigms: Mechanism Design
• Multiagent Systems: Mechanism Design
• Search and Optimization: Metareasoning and Metaheuristics

Application Category

• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

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.

Problem

• Reducing impermanent loss in decentralized exchanges
• Proposing dynamic fee mechanisms over fixed fees
• Enhancing liquidity provider profitability and market efficiency

Innovation

• Dynamic fee mechanisms replace fixed fees
• Asymmetric fees adapt to block and deal
• Simulation framework compares fee algorithms