A game-theoretic analysis of the market microstructure in decentralized exchanges with concentrated liquidity

By Mikhail Belokon, Rostislav Berezovskiy

Rating

1872
Battle Count: 50

Relevance

9/10
Highly relevant for quantitative traders and liquidity providers in DeFi. It provides a rigorous framework for determining optimal range widths and entry/exit thresholds based on observable market metrics (volatility, volume, liquidity), directly impacting strategy profitability in concentrated liquidity AMMs.

Implementation Complexity

7/10
The theoretical model involves solving coupled HJB and KFE equations and deriving closed-form solutions for Markov chains. Implementing the empirical validation requires processing large-scale on-chain data (swap events, pool states) and calculating rolling volatility and liquidity metrics accurately.

Reproducibility

3/5
The paper provides detailed mathematical derivations and specifies the data sources (on-chain data from Ethereum and Base). However, no public code repository URL is explicitly provided in the text for the analysis scripts. The methodology is fully described, allowing for theoretical reproduction, but empirical replication requires access to specific on-chain data indices or subgraphs.

Abstract

We study strategic liquidity provision in decentralized exchanges with concentrated liquidity as a mean-field game. Liquidity providers choose the width of their price range and when to rebalance it, pay a transaction cost at each rebalancing, and share the fees collected at each tick in proportion to their liquidity, so that each provider's income depends on the state of the population. We formulate the problem as a controlled continuous-time Markov chain on a finite state space. In an averaged version of the game, in which a rebalanced position is re-centered at a draw from the long-run law of the price, a mean-field equilibrium exists. Because each provider's share of the fees falls as competing liquidity is added, the equilibrium liquidity profile is unique. For the exact dynamics, with rebalancing at the range boundary and fast execution, we obtain closed forms for the optimal width, the value function and the stationary law of a position, together with a critical volatility above which provision does not pay. Free entry holds a pool strictly below this threshold, at a margin set by the rebalancing cost. The critical volatility is computed from the fee tier, traded volume, active-tick liquidity and price alone, with no fitted parameter. On two and a half years of hourly data from five WETH/USDC pools on Ethereum and Base, it tracks realized volatility. On the Ethereum 0.3% pool the ratio of realized to critical volatility is less than one, and optimal value w^* exists. On the standard Uniswap fee tiers the critical volatility coincides with the on-chain implied volatility of the pool.

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