IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2601.18991.html

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

Author

Listed:
  • 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 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.

Suggested Citation

  • Hardhik Mohanty & Bhaskar Krishnamachari, 2026. "Who Restores the Peg? A Mean-Field Game Approach to Model Stablecoin Market Dynamics," Papers 2601.18991, arXiv.org.
  • Handle: RePEc:arx:papers:2601.18991
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2601.18991
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. A. Bensoussan & K. C. J. Sung & S. C. P. Yam & S. P. Yung, 2016. "Linear-Quadratic Mean Field Games," Journal of Optimization Theory and Applications, Springer, vol. 169(2), pages 496-529, May.
    2. Rene Carmona & Francois Delarue & Daniel Lacker, 2016. "Mean field games of timing and models for bank runs," Papers 1606.03709, arXiv.org, revised Jan 2017.
    3. Makarov, Igor & Schoar, Antoinette, 2020. "Trading and arbitrage in cryptocurrency markets," LSE Research Online Documents on Economics 100409, London School of Economics and Political Science, LSE Library.
    4. Makarov, Igor & Schoar, Antoinette, 2020. "Trading and arbitrage in cryptocurrency markets," Journal of Financial Economics, Elsevier, vol. 135(2), pages 293-319.
    5. Robert Almgren, 2003. "Optimal execution with nonlinear impact functions and trading-enhanced risk," Applied Mathematical Finance, Taylor & Francis Journals, vol. 10(1), pages 1-18.
    6. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    7. 'Alvaro Cartea & Fayc{c}al Drissi & Marcello Monga, 2023. "Decentralised Finance and Automated Market Making: Execution and Speculation," Papers 2307.03499, arXiv.org, revised Jun 2025.
    8. Matteo Richiardi & Roberto Leombruni & Nicole J. Saam & Michele Sonnessa, 2006. "A Common Protocol for Agent-Based Social Simulation," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 9(1), pages 1-15.
    9. Lyons, Richard K. & Viswanath-Natraj, Ganesh, 2023. "What keeps stablecoins stable?," Journal of International Money and Finance, Elsevier, vol. 131(C).
    10. Cartea, Álvaro & Drissi, Fayçal & Monga, Marcello, 2025. "Decentralised finance and automated market making: Execution and speculation," Journal of Economic Dynamics and Control, Elsevier, vol. 177(C).
    11. Emilio Barucci & Giancarlo Giuffra Moncayo & Daniele Marazzina, 2023. "Market impact and efficiency in cryptoassets markets," Digital Finance, Springer, vol. 5(3), pages 519-562, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chu, Gang & Dowling, Michael & Li, Xiao, 2026. "Impermanent loss in cryptocurrency," Journal of International Money and Finance, Elsevier, vol. 160(C).
    2. Ma, Shiqun & Feng, Chao & Xiang, Lijin & Yin, Zhichao, 2025. "Causality and dynamic volatility spillover between the cryptocurrency implied exchange rate and the official exchange rate," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 666(C).
    3. Qi Deng & Zhong-guo Zhou, 2025. "Liquidity-adjusted Return and Volatility, and Autoregressive Models," Papers 2503.08693, arXiv.org.
    4. Castillo León, Jaime & Lehar, Alfred, 2026. "What data have told us about decentralized finance," Journal of Corporate Finance, Elsevier, vol. 96(C).
    5. Kang, Chang-Mo & Kang, Hyoung-Goo & Kim, Donghyun & Sul, Hong Kee, 2025. "Stablecoin and cross-border crypto market integration," Economics Letters, Elsevier, vol. 257(C).
    6. Bastien Baude & Damien Challet & Ioane Muni Toke, 2026. "Optimal execution on Uniswap v2/v3 under transient price impact," Papers 2601.03799, arXiv.org.
    7. Chen, Yu-Lun & Chang, Yung Ting & Yang, J. Jimmy, 2023. "Cryptocurrency hacking incidents and the price dynamics of Bitcoin spot and futures," Finance Research Letters, Elsevier, vol. 55(PB).
    8. Jovanka Lili Matic & Natalie Packham & Wolfgang Karl Härdle, 2023. "Hedging cryptocurrency options," Review of Derivatives Research, Springer, vol. 26(1), pages 91-133, April.
    9. Julia Reynolds & Leopold Sögner & Martin Wagner, 2021. "Deviations from Triangular Arbitrage Parity in Foreign Exchange and Bitcoin Markets," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 13(2), pages 105-146, June.
    10. Hanna Halaburda & Guillaume Haeringer & Joshua Gans & Neil Gandal, 2022. "The Microeconomics of Cryptocurrencies," Journal of Economic Literature, American Economic Association, vol. 60(3), pages 971-1013, September.
    11. Huang, Guan-Ying & Gau, Yin-Feng & Wu, Zhen-Xing, 2022. "Price discovery in fiat currency and cryptocurrency markets," Finance Research Letters, Elsevier, vol. 47(PA).
    12. Hadhri, Sinda & Younus, Mehak & Naeem, Muhammad Abubakr & Yarovaya, Larisa, 2025. "Listening to the Market: Music sentiment and cryptocurrency returns," Journal of International Money and Finance, Elsevier, vol. 157(C).
    13. Saggese, Pietro & Belmonte, Alessandro & Dimitri, Nicola & Facchini, Angelo & Böhme, Rainer, 2023. "Arbitrageurs in the Bitcoin ecosystem: Evidence from user-level trading patterns in the Mt. Gox exchange platform," Journal of Economic Behavior & Organization, Elsevier, vol. 213(C), pages 251-270.
    14. Guo, Weiwei & Intini, Silvia & Jahanshahloo, Hossein, 2025. "Bitcoin arbitrage and exchange default risk," Finance Research Letters, Elsevier, vol. 71(C).
    15. Yeguang Chi & Wenyan Hao & Jiangdong Hu & Zhenkai Ran, 2023. "An empirical investigation on risk factors in cryptocurrency futures," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 43(8), pages 1161-1180, August.
    16. Ruofei Ma & Zhebiao Cai & Wenpin Tang & David Yao, 2025. "Optimal Decisions for Liquid Staking: Allocation and Exit Timing," Papers 2507.14810, arXiv.org, revised Dec 2025.
    17. Guo, Li & Sang, Bo & Tu, Jun & Wang, Yu, 2024. "Cross-cryptocurrency return predictability," Journal of Economic Dynamics and Control, Elsevier, vol. 163(C).
    18. Chen, Bin-xia & Sun, Yan-lin, 2024. "Risk characteristics and connectedness in cryptocurrency markets: New evidence from a non-linear framework," The North American Journal of Economics and Finance, Elsevier, vol. 69(PA).
    19. Dunbar, Kwamie & Owusu-Amoako, Johnson, 2022. "Cryptocurrency returns under empirical asset pricing," International Review of Financial Analysis, Elsevier, vol. 82(C).
    20. Bertrand Crettez & Lisa Morhaim, 2022. "General equilibrium cryptocurrency pricing in an OLG model," Post-Print hal-04103599, HAL.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2601.18991. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.