Open vs. Sealed: Auction Format Choice for Maximal Extractable Value

Abstract

We study optimal auction design for Maximum Extractable Value (MEV) auction markets on Ethereum. Using a dataset of 2.2 million transactions across three major orderflow providers, we establish three empirical regularities: extracted values follow a log-normal distribution with extreme right-tail concentration, competition intensity varies substantially across MEV types, and the standard Revenue Equivalence Theorem breaks down due to affiliation among searchers' valuations. We model this affiliation through a Gaussian common factor, deriving equilibrium bidding strategies and expected revenues for five auction formats, first-price sealed-bid, second-price sealed-bid, English, Dutch, and all-pay, across a fine grid of bidder counts $n$ and affiliation parameters $ρ$. Our simulations confirm the Milgrom-Weber linkage principle: English and second-price sealed-bid auctions strictly dominate Dutch and first-price sealed-bid formats for any $ρ> 0$, with a linkage gap of 14-28\% at moderate affiliation ($ρ=0.5$) and up to 30\% for small bidder counts. Applied to observed bribe totals, this gap corresponds to \$10-18 million in foregone revenue over the sample period. We also document a novel non-monotonicity: at large $n$ and high $ρ$, revenue peaks in the interior of the affiliation parameter space and declines thereafter, as near-perfect correlation collapses the order-statistic spread that drives competitive payments.

Figures (7)

• Figure 1: Analysis by MEV type. Left: total revenue. Middle: transaction count. Right: average bribe percentage.
• Figure 2: MEV value distribution. Left: histogram of log-transformed extracted values with fitted normal density (red curve), supporting the log-normal specification. Right: empirical CDF on logarithmic scale, showing that the distribution spans from below $10^{-5}$ to above $10^{7}$ USDC.
• Figure 3: Revenue concentration. Left: Pareto curve showing cumulative revenue share as a function of the top $x$% of transactions. The curve rises steeply: the top 1% accounts for 68% of total value. Right: revenue share of the top 1%, 5%, 10%, 20%, and 50% of transactions. The dashed red line marks the 50% threshold.
• Figure 4: Expected revenue vs $\rho$ for different values of competition level, $n$. Blue line stands for English and SPSB auctions, red line stands for Dutch and FPSB auctions, and green line stands for All-pay auction (IPV benchmark, does not change with $\rho$).
• Figure 5: Expected revenue vs $n$ for different values of affiliation, $\rho$. Blue line stands for English and SPSB auctions, red line stands for Dutch and FPSB auctions, and green line stands for All-pay auction (IPV benchmark, does not change with $\rho$).