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UNISWAP: Impermanent Loss and Risk Profile of a Liquidity Provider

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arxiv 2106.14404 v1 pith:E5CYCWTE submitted 2021-06-28 q-fin.TR q-fin.CPq-fin.GNq-fin.PMq-fin.RM

Andreas A. Aigner , Gurvinder Dhaliwal This is my paper · ORCID
classification q-fin.TRq-fin.CPq-fin.GNq-fin.PMq-fin.RM
keywords liquidityuniswapcurrenciesdecentralizedexchangefixedfunctionimpermanent
verification ladder T0 review T1 audit T2 compute T3 formal
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Uniswap is a decentralized exchange (DEX) and was first launched on November 2, 2018 on the Ethereum mainnet [1] and is part of an Ecosystem of products in Decentralized Finance (DeFi). It replaces a traditional order book type of trading common on centralized exchanges (CEX) with a deterministic model that swaps currencies (or tokens/assets) along a fixed price function determined by the amount of currencies supplied by the liquidity providers. Liquidity providers can be regarded as investors in the decentralized exchange and earn fixed commissions per trade. They lock up funds in liquidity pools for distinct pairs of currencies allowing market participants to swap them using the fixed price function. Liquidity providers take on market risk as a liquidity provider in exchange for earning commissions on each trade. Here we analyze the risk profile of a liquidity provider and the so called impermanent (unrealized) loss in particular. We provide an improved version of the commonly denoted impermanent loss function for Uniswap v2 on the semi-infinite domain. The differences between Uniswap v2 and v3 are also discussed.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. ZAPs: A Reward Attribution Framework for DeFi Ecosystems with Adversarial-Robust Scoring via Parallel Anomaly Ensemble Detection

    q-fin.GN 2026-07 conditional novelty 5.0 of 10

    ZAPs attributes DeFi rewards via percentile-normalized score and a four-layer adversarial defense, reporting 0.923 ensemble ROC-AUC and 30–90% cuts in simulated adversarial reward capture.

  2. Deep Reputation Scoring in DeFi: zScore-Based Wallet Ranking from Liquidity and Trading Signals

    q-fin.GN 2025-07 reject novelty 4.0 of 10

    A two-score system for Uniswap wallets is built from rule-based blueprints refined by a deep residual network, with validation limited to reproducing the rules.

  3. Improving DeFi Accessibility through Efficient Liquidity Provisioning with Deep Reinforcement Learning

    q-fin.CP 2025-01 conditional novelty 4.0 of 10

    A PPO-trained agent that dynamically adjusts Uniswap v3 liquidity ranges beat a fixed 500-hour periodic rebalancing heuristic in 7 of 11 out-of-sample windows on WETH/USDC.

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