IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2105.02782.html
   My bibliography  Save this paper

The Homogenous Properties of Automated Market Makers

Author

Listed:
  • Johannes Rude Jensen
  • Mohsen Pourpouneh
  • Kurt Nielsen
  • Omri Ross

Abstract

Automated market makers (AMM) have grown to obtain significant market share within the cryptocurrency ecosystem, resulting in a proliferation of new products pursuing exotic strategies for horizontal differentiation. Yet, their theoretical properties are curiously homogeneous when a set of basic assumptions are met. In this paper, we start by presenting a universal approach to deriving a formula for liquidity provisioning for AMMs. Next, we show that the constant function market maker and token swap market maker models are theoretically equivalent when liquidity reserves are uniform. Proceeding with an examination of AMM market microstructure, we show how non-linear price effect translates into slippage for traders and impermanent losses for liquidity providers. We proceed by showing how impermanent losses are a function of both volatility and market depth and discuss the implications of these findings within the context of the literature.

Suggested Citation

  • Johannes Rude Jensen & Mohsen Pourpouneh & Kurt Nielsen & Omri Ross, 2021. "The Homogenous Properties of Automated Market Makers," Papers 2105.02782, arXiv.org.
    • Handle: RePEc:arx:papers:2105.02782
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2105.02782
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Joon Chae, 2005. "Trading Volume, Information Asymmetry, and Timing Information," Journal of Finance, American Finance Association, vol. 60(1), pages 413-442, February.
    2. Lee, Jihui & Li, Gen & Wilson, James D., 2020. "Varying-coefficient models for dynamic networks," Computational Statistics & Data Analysis, Elsevier, vol. 152(C).
    3. Aming Li & Yang-Yu Liu, 2020. "Controlling Network Dynamics," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 22(07n08), pages 1-19, February.
    4. Massimo Bartoletti & James Hsin-yu Chiang & Alberto Lluch-Lafuente, 2020. "SoK: Lending Pools in Decentralized Finance," Papers 2012.13230, arXiv.org.
    5. Guillermo Angeris & Tarun Chitra, 2020. "Improved Price Oracles: Constant Function Market Makers," Papers 2003.10001, arXiv.org, revised Jun 2020.
    6. Ge, S. & Li, S. & Linton, O., 2020. "A Dynamic Network of Arbitrage Characteristics," Cambridge Working Papers in Economics 2060, Faculty of Economics, University of Cambridge.
    7. Robin Hanson, 2003. "Combinatorial Information Market Design," Information Systems Frontiers, Springer, vol. 5(1), pages 107-119, January.
    8. Kyle, Albert S, 1985. "Continuous Auctions and Insider Trading," Econometrica, Econometric Society, vol. 53(6), pages 1315-1335, November.
    9. Alex Evans, 2020. "Liquidity Provider Returns in Geometric Mean Markets," Papers 2006.08806, arXiv.org, revised Jul 2020.
    10. Guillermo Angeris & Alex Evans & Tarun Chitra, 2020. "When does the tail wag the dog? Curvature and market making," Papers 2012.08040, arXiv.org.
    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. Arman Abgaryan & Utkarsh Sharma, 2023. "Dynamic Function Market Maker," Papers 2307.13624, 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. Sam M. Werner & Daniel Perez & Lewis Gudgeon & Ariah Klages-Mundt & Dominik Harz & William J. Knottenbelt, 2021. "SoK: Decentralized Finance (DeFi)," Papers 2101.08778, arXiv.org, revised Sep 2022.
    2. Guillermo Angeris & Akshay Agrawal & Alex Evans & Tarun Chitra & Stephen Boyd, 2021. "Constant Function Market Makers: Multi-Asset Trades via Convex Optimization," Papers 2107.12484, arXiv.org.
    3. Guillermo Angeris & Alex Evans & Tarun Chitra, 2020. "When does the tail wag the dog? Curvature and market making," Papers 2012.08040, arXiv.org.
    4. Daniel Kirste & Niclas Kannengie{ss}er & Ricky Lamberty & Ali Sunyaev, 2023. "How Automated Market Makers Approach the Thin Market Problem in Cryptoeconomic Systems," Papers 2309.12818, arXiv.org, revised Sep 2023.
    5. Arman Abgaryan & Utkarsh Sharma, 2023. "Dynamic Function Market Maker," Papers 2307.13624, arXiv.org.
    6. Guillermo Angeris & Alex Evans & Tarun Chitra, 2021. "Replicating Market Makers," Papers 2103.14769, arXiv.org.
    7. Neelesh Tiruviluamala & Alexander Port & Erik Lewis, 2022. "A General Framework for Impermanent Loss in Automated Market Makers," Papers 2203.11352, arXiv.org.
    8. Baruch, Shmuel & Panayides, Marios & Venkataraman, Kumar, 2017. "Informed trading and price discovery before corporate events," Journal of Financial Economics, Elsevier, vol. 125(3), pages 561-588.
    9. Lof, Matthijs & van Bommel, Jos, 2023. "Asymmetric information and the distribution of trading volume," Journal of Corporate Finance, Elsevier, vol. 82(C).
    10. Michael Ostrovsky, 2012. "Information Aggregation in Dynamic Markets With Strategic Traders," Econometrica, Econometric Society, vol. 80(6), pages 2595-2647, November.
    11. Galanis, S. & Ioannou, C. & Kotronis, S., 2019. "Information Aggregation Under Ambiguity: Theory and Experimental Evidence," Working Papers 20/05, Department of Economics, City University London.
    12. Qin Wang & Hsiao-Fen Yang, 2015. "Earnings announcements, trading volume, and price discovery: evidence from dual class firms," Review of Quantitative Finance and Accounting, Springer, vol. 44(4), pages 669-700, May.
    13. Mudalige, Priyantha & Duong, Huu Nhan & Kalev, Petko S. & Gupta, Kartick, 2020. "Who trades in competing firms around earnings announcements," Pacific-Basin Finance Journal, Elsevier, vol. 59(C).
    14. Galanis Spyros & Kotronis Stelios, 2021. "Updating Awareness and Information Aggregation," The B.E. Journal of Theoretical Economics, De Gruyter, vol. 21(2), pages 613-635, June.
    15. Choi, Darwin, 2019. "Disposition sales and stock market liquidity," Journal of Financial Markets, Elsevier, vol. 45(C), pages 19-36.
    16. Panayiotis Papakyriacou & George Nishiotis & Andreas Milidonis & Alex Michaelides, 2012. "Sovereign Debt Rating Changes and the Stock Market," 2012 Meeting Papers 522, Society for Economic Dynamics.
    17. Hu, Conghui & Liu, Yu-Jane & Zhu, Ning, 2019. "De-Leverage and illiquidity contagion," Journal of Banking & Finance, Elsevier, vol. 102(C), pages 1-18.
    18. Danny Lo, 2015. "Essays in Market Microstructure and Investor Trading," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 4-2015.
    19. Thusitha Mahipala & Howard Chan & Robert Faff, 2009. "Trading volume and information asymmetry: routine versus nonroutine earnings announcements in Australia," Applied Financial Economics, Taylor & Francis Journals, vol. 19(21), pages 1737-1752.
    20. Paul J. Healy & Sera Linardi & J. Richard Lowery & John O. Ledyard, 2010. "Prediction Markets: Alternative Mechanisms for Complex Environments with Few Traders," Management Science, INFORMS, vol. 56(11), pages 1977-1996, November.

    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:2105.02782. 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.