Who Restores the Peg? A Mean-Field Game Approach to Model Stablecoin Market Dynamics

TL;DR

This paper models stablecoin de-pegging and recovery using a mean-field game framework, revealing how market participants' strategic interactions influence stability and identifying thresholds for systemic failure.

Contribution

It introduces a novel mean-field game model for stablecoin markets that captures endogenous market frictions and explains peg recovery dynamics during stress events.

Findings

The model reproduces observed recovery times from historical de-pegs.

Primary-market arbitrage predominantly stabilizes the peg during stress.

Identifies a non-linear threshold where recovery becomes significantly slower.

Abstract

USDC and USDT are the dominant stablecoins pegged to \$1 with a total market capitalization of over \$300B and rising. Stablecoins make dollar value globally accessible with secure transfer and settlement. Yet in practice, these stablecoins experience periods of stress and de-pegging from their \$1 target, posing significant systemic risks. The behavior of market participants during these stress events and the collective actions that either restore or break the peg are not well understood. This paper addresses the question: who restores the peg?. We develop a dynamic, agent-based mean-field game framework for fiat-collateralized stablecoins, in which a large population of arbitrageurs and retail traders strategically interact across primary and secondary markets during a de-peg episode. The key advantage of this equilibrium formulation is that it endogenously maps market frictions into a market-clearing price path and implied net order flows, allowing us to attribute peg-reverting pressure by channel and to stress-test when a given infrastructure becomes insufficient for recovery. Using three historical de-peg events, we show that the calibrated equilibrium reproduces observed recovery half-lives and yields an order flow decomposition in which system-wide stress is predominantly stabilized by primary-market arbitrage. Finally, a quantitative sensitivity analysis identifies a non-linear breakdown threshold, beyond which a de-peg becomes markedly slower to reverse.