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

2026-01-26

AI Summary

This study addresses the unclear recovery mechanisms of stablecoins following de-pegging events and their associated systemic risks. It introduces, for the first time, a mean-field game framework to model the stablecoin market as a dynamic agent-based system comprising a continuum of arbitrageurs and retail traders. The model endogenously captures strategic interactions, price dynamics, and order flow across both primary (minting/redemption) and secondary markets, while incorporating realistic market frictions. Calibrated against historical de-pegging episodes and validated through sensitivity analyses, the model successfully replicates the observed half-lives of recovery in three major incidents. It reveals that primary-market arbitrage primarily drives re-pegging; however, when this channel is impaired, coordinated activity across both primary and secondary markets becomes essential. The analysis further identifies a nonlinear failure threshold in primary-market frictions beyond which recovery dynamics deteriorate sharply.

Technology Category

• Multiagent Systems: Mechanism Design
• Game Theory and Economic Paradigms: Mechanism Design
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions
• Security and Privacy: Cryptocurrency and smart contracts
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

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 interacts across explicit primary (mint/redeem) and secondary (exchange) 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 mechanism 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, whereas episodes with impaired primary redemption require a joint recovery via both primary and secondary markets. Finally, a quantitative sensitivity analysis of primary-rail frictions identifies a non-linear breakdown threshold. Beyond this point, secondary-market liquidity acts mainly as a second-order amplifier around this primary-market bottleneck.

Problem

• stablecoin
• de-pegging
• market dynamics
• mean-field game
• arbitrage

Innovation

• mean-field game
• stablecoin
• market microstructure
• arbitrage
• peg restoration