Paper: arXiv 2601.18991

Authors: Hardhik Mohanty, Bhaskar Krishnamachari

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.

Complexity vs Empirical Score

  • Math Complexity: 7.5/10
  • Empirical Rigor: 7.0/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper presents a novel Mean-Field Game (MFG) formulation to model stablecoin de-pegging dynamics endogenously, combining high mathematical rigor with quantitative calibration across historical de-peg events. It successfully identifies non-linear breakdown thresholds in primary arbitrage rails, bridging multi-agent strategic interactions with empirical market microstructure.

Research Flowchart

  flowchart TD
    A["Research Goal: Who Restores the Stablecoin Peg?"] --> B["Mean-Field Game Framework<br/>(Arbitrageurs & Retail Traders)"]
    C["Inputs: Primary/Secondary Markets &<br/>3 Historical De-peg Events"] --> B
    B --> D["Calibrate Equilibrium &<br/>Simulate Price Paths"]
    D --> E["Outcome 1: Primary-market arbitrage<br/>drives peg restoration"]
    D --> F["Outcome 2: Non-linear threshold<br/>identified where recovery fails"]

Cited References (35)

  1. Taming Wildcat Stablecoins (2023)
  2. Crypto Lending and Stablecoin De-Pegging: Key Risks and Challenges (2024)
  3. What Keeps Stablecoins Stable? (2023)
  4. What Drives the (In)Stability of a Stablecoin? (2024)
  5. Learning in Mean Field Games: A Survey (2022) — link
  6. Linear-Quadratic Mean Field Games (2016)
  7. Stablecoins: Growth Potential and Impact on Banking (2022)
  8. Primary and Secondary Markets for Stablecoins (2024)
and 27 more
  1. Currency Stability Using Blockchain Technology (2022)
  2. Modeling and Analysis of Crypto-Backed Over-Collateralized Stable Derivatives in DeFi — → 2402.18119
  3. Trading and Arbitrage in Cryptocurrency Markets (2020)
  4. On Stablecoin Price Processes and Arbitrage (2021)
  5. Mean Field Games (2007)
  6. Mean Field Games of Timing and Models for Bank Runs (2017)
  7. Mean Field Game of Controls and an Application to Trade Crowding (2018)
  8. A Token Economics Explanation for the De-Pegging of the Algorithmic Stablecoin: Analysis of the Case of Terra (2023)
  9. Break a Peg! A Study of Stablecoin Co-Instability (2024)
  10. Interconnected DeFi: Ripple Effects from the Terra Collapse (2023)
  11. Agent-Based Computational Finance (2006)
  12. Daisim: A Computational Simulator for the MakerDAO Stablecoin (2021)
  13. Algorithmic Stablecoins: A Simulator for the Dual-Token Model in Normal and Panic Scenarios (2025)
  14. A Policy Iteration Method for Mean Field Games (2021)
  15. Market Impact and Efficiency in Cryptoassets Markets (2023)
  16. Exchange Market Liquidity Prediction with the K-Nearest Neighbor Approach: Crypto vs. Fiat Currencies (2020)
  17. Decentralised Finance and Automated Market Making: Execution and Speculation (2025)
  18. Liquidity Shocks, Token Returns and Market Capitalization in Decentralized Finance (DeFi) Markets (2022)
  19. Generalized Autoregressive Conditional Heteroskedasticity (1986)
  20. Differential Evolution: A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces (1997)
  21. Solving Continuous Mean Field Games: Deep Reinforcement Learning for Non-Stationary Dynamics (2025)
  22. Reinforcement Learning in Non-Stationary Discrete-Time Linear-Quadratic Mean-Field Games (2020)
  23. Historical Market Data (2025)
  24. Optimal Execution with Nonlinear Impact Functions and Trading-Enhanced Risk (2003)
  25. Analysis of Financial Time Series (2010)
  26. A common protocol for agent-based social simulation (2006)
  27. stablecoin-peg-mfg (2026)

1 of 35 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.