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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,742 papers · 148 categories

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4 results for “CLMMs”

A mathematical framework for modelling CLMM dynamics in continuous timeq-fin.MF

Develops a mathematical model for CLMM dynamics in DeFi.

problem Analyzing CLMMs in continuous time trading.

method Modeling CLMM dynamics as measure-valued processes, examining three arbitrage models.

result Trading fees limit admissible price processes, impacting CLMM design.

Decentralized Finance CLMMs Continuous-time Arbitrage Mathematical modeling

2024-12-24abs ↗pdf ↗

Backtesting Framework for Concentrated Liquidity Market Makers on Uniswap V3 Decentralized Exchangeq-fin.MF

Backtesting framework for CLMMs on Uniswap V3 reduces reward estimation error.

problem Estimating rewards for CLMMs in Uniswap V3 liquidity pools.

method Parametric model for liquidity distribution, historical data analysis.

result Error in reward estimation less than 1% for each pool.

Uniswap V3 Concentrated Liquidity Market Makers Liquidity Rewards Backtesting Automated Market Making

2024-10-13abs ↗pdf ↗

Liquidity provision in CLMMs: evidence from transactions dataq-fin.TR

Study on liquidity providers' performance in decentralized exchanges.

problem Unclear profitability of liquidity providers in decentralized exchanges.

method Reconstructing LP PnL dynamics from on-chain events, introducing a new metric.

result Only about one out of six LPs avoids losses, suggesting open questions about LP participation motives.

Decentralized exchanges Liquidity provision Uniswap PancakeSwap SushiSwap

2026-04-23abs ↗pdf ↗

Dynamic Liquidity Provision in Decentralized Markets: Strategy Optimization and Performance Evaluation in Concentrated Liquidity AMMsq-fin.MF

Novel method reconstructs liquidity data for CLMMs, optimizing dynamic liquidity strategies.

problem Challenges in evaluating and optimizing CLMMs due to lack of historical liquidity data.

method Reconstructs historical liquidity states from swap transaction data using machine learning.

result Identifies outperformance of dynamic liquidity strategies over uniform allocation benchmarks.

Decentralized Finance Concentrated Liquidity Market Microstructure Machine Learning Impermanent Loss

2025-05-21abs ↗pdf ↗