Where Does MEV Really Come From? Revisiting CEXDEX Arbitrage on Ethereum
Bence Ladóczk, Miklós Rásonyi, János Tapolcai
TLDR
This paper re-evaluates CEX-DEX arbitrage on Ethereum, proposing a new model that accounts for price jumps to explain higher MEV profits.
Key contributions
- Shows prior AMM models underestimated CEX-DEX arbitrage by ignoring critical price jumps.
- Presents an extended discrete-time AMM model with diffusive and stochastic jump components.
- Proves the mispricing process in the new framework is an ergodic Markov chain.
- Implemented model in C++ accurately fits Ethereum data, providing a theoretical MEV explanation.
Why it matters
This paper offers a crucial theoretical explanation for the origins of Maximal Extractable Value (MEV) on Ethereum, particularly from CEX-DEX arbitrage. By correcting prior model limitations, it provides a more accurate understanding of how significant MEV profits arise, which is vital for ecosystem health.
Original Abstract
A central question of the Ethereum ecosystem is where Maximal Extractable Value (MEV)revenue originates and to what extent it stems from harming unsuspecting users. It is acceptable if MEV arises from arbitrages between centralised and decentralised exchanges (CEX-DEX). Yet theoretical models have significantly underestimated the scale of these arbitrages, while empirical studies have highlighted their importance - though these remain conservative estimates, constrained by numerous debatable heuristic assumptions. Revisiting the theoretical model, we found that CEX-DEX arbitrages require trading volumes on the order of the total activity of major liquidity pools and yield profits comparable to MEV. Most prior AMM models utilised the Black-Scholes (BS) stochastic differential equation (SDE) - i.e., geometric Brownian motion - and assumed continuous price trajectories where asset prices move in small increments only.We argue that BS underestimates arbitrage profits by ignoring price jumps, which are precisely the points at which arbitrage opportunities tend to arise. To address this gap, we present an extended discrete-time AMM model in which the price process is the sum of a diffusive component and stochastic jumps that can have arbitrary noise distributions. Although mathematically more involved this framework allows us to employ a general discrete-time SDE and compute the stationary probability distribution via function iteration with geometric convergence. We further prove that the resulting mispricing process is an ergodic Markov chain. We implement our model in C++, collect spot prices and AMM exchange data from the Ethereum blockchain and fit the model parameters to the observed prices. The estimates derived from our model closely match empirical observations and provide a natural theoretical explanation for several fundamental questions in the blockchain ecosystem.
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