IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2112.02961.html
   My bibliography  Save this paper

Closed-Loop Nash Competition for Liquidity

Author

Listed:
  • Alessandro Micheli
  • Johannes Muhle-Karbe
  • Eyal Neuman

Abstract

We study a multi-player stochastic differential game, where agents interact through their joint price impact on an asset that they trade to exploit a common trading signal. In this context, we prove that a closed-loop Nash equilibrium exists if the price impact parameter is small enough. Compared to the corresponding open-loop Nash equilibrium, both the agents' optimal trading rates and their performance move towards the central-planner solution, in that excessive trading due to lack of coordination is reduced. However, the size of this effect is modest for plausible parameter values.

Suggested Citation

  • Alessandro Micheli & Johannes Muhle-Karbe & Eyal Neuman, 2021. "Closed-Loop Nash Competition for Liquidity," Papers 2112.02961, arXiv.org, revised Jun 2023.
    • Handle: RePEc:arx:papers:2112.02961
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2112.02961
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Alexander Lipton & Umberto Pesavento & Michael G Sotiropoulos, 2013. "Trade arrival dynamics and quote imbalance in a limit order book," Papers 1312.0514, arXiv.org.
    2. David Evangelista & Yuri Thamsten, 2020. "On finite population games of optimal trading," Papers 2004.00790, arXiv.org, revised Feb 2021.
    3. Nicolae Gârleanu & Lasse Heje Pedersen, 2013. "Dynamic Trading with Predictable Returns and Transaction Costs," Journal of Finance, American Finance Association, vol. 68(6), pages 2309-2340, December.
    4. Collin-Dufresne, Pierre & Daniel, Kent & Sağlam, Mehmet, 2020. "Liquidity regimes and optimal dynamic asset allocation," Journal of Financial Economics, Elsevier, vol. 136(2), pages 379-406.
    5. Philippe Casgrain & Sebastian Jaimungal, 2020. "Mean‐field games with differing beliefs for algorithmic trading," Mathematical Finance, Wiley Blackwell, vol. 30(3), pages 995-1034, July.
    6. Charles-Albert Lehalle & Eyal Neuman, 2019. "Incorporating signals into optimal trading," Finance and Stochastics, Springer, vol. 23(2), pages 275-311, April.
    7. Daniel Lacker & Thaleia Zariphopoulou, 2019. "Mean field and n‐agent games for optimal investment under relative performance criteria," Mathematical Finance, Wiley Blackwell, vol. 29(4), pages 1003-1038, October.
    8. Joachim de Lataillade & Cyril Deremble & Marc Potters & Jean-Philippe Bouchaud, 2012. "Optimal Trading with Linear Costs," Papers 1203.5957, arXiv.org.
    9. Bruce Ian Carlin & Miguel Sousa Lobo & S. Viswanathan, 2007. "Episodic Liquidity Crises: Cooperative and Predatory Trading," Journal of Finance, American Finance Association, vol. 62(5), pages 2235-2274, October.
    10. Nicholas Barberis, 2000. "Investing for the Long Run when Returns Are Predictable," Journal of Finance, American Finance Association, vol. 55(1), pages 225-264, February.
    11. Philippe Casgrain & Sebastian Jaimungal, 2018. "Mean Field Games with Partial Information for Algorithmic Trading," Papers 1803.04094, arXiv.org, revised Mar 2019.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Puru Gupta & Saul D. Jacka, 2023. "Portfolio Choice In Dynamic Thin Markets: Merton Meets Cournot," Papers 2309.16047, arXiv.org.
    2. Guanxing Fu & Paul P. Hager & Ulrich Horst, 2024. "A Mean-Field Game of Market Entry: Portfolio Liquidation with Trading Constraints," Papers 2403.10441, arXiv.org.
    3. Joseph Jerome & Leandro Sanchez-Betancourt & Rahul Savani & Martin Herdegen, 2022. "Model-based gym environments for limit order book trading," Papers 2209.07823, arXiv.org.

    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. Rama Cont & Alessandro Micheli & Eyal Neuman, 2022. "Fast and Slow Optimal Trading with Exogenous Information," Papers 2210.01901, arXiv.org, revised Jun 2023.
    2. Johannes Muhle-Karbe & Xiaofei Shi & Chen Yang, 2020. "An Equilibrium Model for the Cross-Section of Liquidity Premia," Papers 2011.13625, arXiv.org.
    3. Johannes Muhle-Karbe & Max Reppen & H. Mete Soner, 2016. "A Primer on Portfolio Choice with Small Transaction Costs," Papers 1612.01302, arXiv.org, revised May 2017.
    4. Guanxing Fu & Ulrich Horst & Xiaonyu Xia, 2020. "Portfolio Liquidation Games with Self-Exciting Order Flow," Papers 2011.05589, arXiv.org.
    5. Yan, Tingjin & Han, Jinhui & Ma, Guiyuan & Siu, Chi Chung, 2023. "Dynamic asset-liability management with frictions," Insurance: Mathematics and Economics, Elsevier, vol. 111(C), pages 57-83.
    6. Ma, Guiyuan & Siu, Chi Chung & Zhu, Song-Ping, 2022. "Portfolio choice with return predictability and small trading frictions," Economic Modelling, Elsevier, vol. 111(C).
    7. Rim Bernoussi & Michael Rockinger, 2023. "Rebalancing with transaction costs: theory, simulations, and actual data," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 37(2), pages 121-160, June.
    8. Joachim de Lataillade & Ayman Chaouki, 2020. "Equations and Shape of the Optimal Band Strategy," Papers 2003.04646, arXiv.org, revised Mar 2020.
    9. Sebastian Jaimungal, 2022. "Reinforcement learning and stochastic optimisation," Finance and Stochastics, Springer, vol. 26(1), pages 103-129, January.
    10. Alain Bensoussan & Guiyuan Ma & Chi Chung Siu & Sheung Chi Phillip Yam, 2022. "Dynamic mean–variance problem with frictions," Finance and Stochastics, Springer, vol. 26(2), pages 267-300, April.
    11. Eyal Neuman & Yufei Zhang, 2023. "Statistical Learning with Sublinear Regret of Propagator Models," Papers 2301.05157, arXiv.org.
    12. Moritz Voß, 2022. "A two-player portfolio tracking game," Mathematics and Financial Economics, Springer, volume 16, number 6, June.
    13. Yaoting Lei & Ya Li & Jing Xu, 2020. "Two Birds, One Stone: Joint Timing of Returns and Capital Gains Taxes," Management Science, INFORMS, vol. 66(2), pages 823-843, February.
    14. Filippo Passerini & Samuel E. Vazquez, 2015. "Optimal Trading with Alpha Predictors," Papers 1501.03756, arXiv.org, revised Jan 2015.
    15. Ludovic Tangpi & Shichun Wang, 2022. "Optimal Bubble Riding: A Mean Field Game with Varying Entry Times," Papers 2209.04001, arXiv.org, revised Jan 2024.
    16. Puru Gupta & Saul D. Jacka, 2023. "Portfolio Choice In Dynamic Thin Markets: Merton Meets Cournot," Papers 2309.16047, arXiv.org.
    17. Bryan Kelly & Semyon Malamud & Lasse Heje Pedersen, 2023. "Principal Portfolios," Journal of Finance, American Finance Association, vol. 78(1), pages 347-387, February.
    18. Ibrahim Ekren & Johannes Muhle-Karbe, 2017. "Portfolio Choice with Small Temporary and Transient Price Impact," Papers 1705.00672, arXiv.org, revised Apr 2020.
    19. Zhang, Jinqing & Jin, Zeyu & An, Yunbi, 2017. "Dynamic portfolio optimization with ambiguity aversion," Journal of Banking & Finance, Elsevier, vol. 79(C), pages 95-109.
    20. Lukas Gonon & Johannes Muhle-Karbe & Xiaofei Shi, 2019. "Asset Pricing with General Transaction Costs: Theory and Numerics," Papers 1905.05027, arXiv.org, revised Apr 2020.

    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:2112.02961. 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.