IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2111.11315.html
   My bibliography  Save this paper

Investing in crypto: speculative bubbles and cyclic stochastic price pumps

Author

Listed:
  • Misha Perepelitsa

Abstract

The problem of investing into a cryptocurrency market requires good understanding of the processes that regulate the price of the currency. In this paper we offer a view of a cryptocurrency market as an environment for realization of a self-organized speculative scheme that results in a formation of a characteristic price bubble as a transient phenomenon. We use microscale, agent-based models to simulate the system behavior and derive macroscale ODE models to estimate such parameters as the return rate and the market value of investments. We provide the formula for the total risk of the system as a sum of two independent components, one being characteristic of the price bubble and the other of the investor behavior.

Suggested Citation

  • Misha Perepelitsa, 2021. "Investing in crypto: speculative bubbles and cyclic stochastic price pumps," Papers 2111.11315, arXiv.org, revised Oct 2022.
    • Handle: RePEc:arx:papers:2111.11315
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2111.11315
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Lux, Thomas, 1998. "The socio-economic dynamics of speculative markets: interacting agents, chaos, and the fat tails of return distributions," Journal of Economic Behavior & Organization, Elsevier, vol. 33(2), pages 143-165, January.
    2. Jose A. Scheinkman & Wei Xiong, 2003. "Overconfidence and Speculative Bubbles," Journal of Political Economy, University of Chicago Press, vol. 111(6), pages 1183-1219, December.
    3. A. Chakraborti & I. Muni-Toke & M. Patriarca & F. Abergel, 2011. "Econophysics Review : II. Agent-based models," Post-Print hal-03332946, HAL.
    4. Bak, P. & Paczuski, M. & Shubik, M., 1997. "Price variations in a stock market with many agents," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 246(3), pages 430-453.
    5. Anirban Chakraborti & Ioane Muni Toke & Marco Patriarca & Frederic Abergel, 2011. "Econophysics review: II. Agent-based models," Quantitative Finance, Taylor & Francis Journals, vol. 11(7), pages 1013-1041.
    6. Olivier J. Blanchard & Mark W. Watson, 1982. "Bubbles, Rational Expectations and Financial Markets," NBER Working Papers 0945, National Bureau of Economic Research, Inc.
    7. 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.
    8. Makarov, Igor & Schoar, Antoinette, 2020. "Trading and arbitrage in cryptocurrency markets," Journal of Financial Economics, Elsevier, vol. 135(2), pages 293-319.
    9. Thomas Lux & Michele Marchesi, 2000. "Volatility Clustering In Financial Markets: A Microsimulation Of Interacting Agents," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 3(04), pages 675-702.
    10. Levy, Moshe & Solomon, Sorin, 1997. "New evidence for the power-law distribution of wealth," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 242(1), pages 90-94.
    11. Levy, Moshe & Levy, Haim & Solomon, Sorin, 1994. "A microscopic model of the stock market : Cycles, booms, and crashes," Economics Letters, Elsevier, vol. 45(1), pages 103-111, May.
    12. Anders Johansen & Olivier Ledoit & Didier Sornette, 2000. "Crashes As Critical Points," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 3(02), pages 219-255.
    13. Misha Perepelitsa, 2021. "Psychological dimension of adaptive trading in cryptocurrency markets," Papers 2109.12166, arXiv.org.
    14. Perepelitsa, Misha & Timofeyev, Ilya, 2019. "Asynchronous stochastic price pump," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 516(C), pages 356-364.
    15. Roth, Alvin E. & Erev, Ido, 1995. "Learning in extensive-form games: Experimental data and simple dynamic models in the intermediate term," Games and Economic Behavior, Elsevier, vol. 8(1), pages 164-212.
    16. Blanchard, Olivier Jean, 1979. "Speculative bubbles, crashes and rational expectations," Economics Letters, Elsevier, vol. 3(4), pages 387-389.
    17. Anirban Chakraborti & Ioane Muni Toke & Marco Patriarca & Frédéric Abergel, 2011. "Econophysics review: II. Agent-based models," Post-Print hal-00621059, HAL.
    18. Levy, Haim & Levy, Moshe & Solomon, Sorin, 2000. "Microscopic Simulation of Financial Markets," Elsevier Monographs, Elsevier, edition 1, number 9780124458901.
    19. Adrian Dragulescu & Victor M. Yakovenko, 2000. "Statistical mechanics of money," Papers cond-mat/0001432, arXiv.org, revised Aug 2000.
    20. Lux, Thomas, 1995. "Herd Behaviour, Bubbles and Crashes," Economic Journal, Royal Economic Society, vol. 105(431), pages 881-896, July.
    21. Thomas Lux & Michele Marchesi, 1999. "Scaling and criticality in a stochastic multi-agent model of a financial market," Nature, Nature, vol. 397(6719), pages 498-500, February.
    22. Sha Wang & Jean-Philippe Vergne, 2017. "Buzz Factor or Innovation Potential: What Explains Cryptocurrencies’ Returns?," PLOS ONE, Public Library of Science, vol. 12(1), pages 1-17, January.
    23. W. Brian Arthur & Paul Tayler, "undated". "Asset Pricing Under Endogenous Expectations in an Artificial Stock Market," Computing in Economics and Finance 1997 57, Society for Computational Economics.
    24. Artzrouni, Marc, 2009. "The mathematics of Ponzi schemes," Mathematical Social Sciences, Elsevier, vol. 58(2), pages 190-201, September.
    25. David Garcia & Frank Schweitzer, 2015. "Social signals and algorithmic trading of Bitcoin," Papers 1506.01513, arXiv.org, revised Sep 2015.
    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. Misha Perepelitsa, 2021. "Psychological dimension of adaptive trading in cryptocurrency markets," Papers 2109.12166, arXiv.org.
    2. Misha Perepelitsa & Ilya Timofeyev, 2022. "Self-sustained price bubbles driven by digital currency innovations and adaptive market behavior," SN Business & Economics, Springer, vol. 2(3), pages 1-15, March.
    3. E. Samanidou & E. Zschischang & D. Stauffer & T. Lux, 2001. "Microscopic Models of Financial Markets," Papers cond-mat/0110354, arXiv.org.
    4. Misha Perepelitsa, 2018. "A model of adaptive, market behavior generating positive returns, volatility and system risk," Papers 1809.09601, arXiv.org.
    5. Misha Perepelitsa & Ilya Timofeyev, 2020. "Self-sustained price bubbles driven by Bitcoin innovations and adaptive behavior," Papers 2012.14860, arXiv.org.
    6. E. Samanidou & E. Zschischang & D. Stauffer & T. Lux, 2007. "Agent-based Models of Financial Markets," Papers physics/0701140, arXiv.org.
    7. Thomas Lux, 2009. "Applications of Statistical Physics in Finance and Economics," Chapters, in: J. Barkley Rosser Jr. (ed.), Handbook of Research on Complexity, chapter 9, Edward Elgar Publishing.
    8. Torsten Trimborn & Philipp Otte & Simon Cramer & Maximilian Beikirch & Emma Pabich & Martin Frank, 2020. "SABCEMM: A Simulator for Agent-Based Computational Economic Market Models," Computational Economics, Springer;Society for Computational Economics, vol. 55(2), pages 707-744, February.
    9. Lux, Thomas & Alfarano, Simone, 2016. "Financial power laws: Empirical evidence, models, and mechanisms," Chaos, Solitons & Fractals, Elsevier, vol. 88(C), pages 3-18.
    10. Torsten Trimborn & Philipp Otte & Simon Cramer & Max Beikirch & Emma Pabich & Martin Frank, 2018. "SABCEMM-A Simulator for Agent-Based Computational Economic Market Models," Papers 1801.01811, arXiv.org, revised Oct 2018.
    11. Lux, Thomas, 2008. "Applications of statistical physics in finance and economics," Kiel Working Papers 1425, Kiel Institute for the World Economy (IfW Kiel).
    12. Troy Tassier, 2013. "Handbook of Research on Complexity, by J. Barkley Rosser, Jr. and Edward Elgar," Eastern Economic Journal, Palgrave Macmillan;Eastern Economic Association, vol. 39(1), pages 132-133.
    13. Detlef Seese & Christof Weinhardt & Frank Schlottmann (ed.), 2008. "Handbook on Information Technology in Finance," International Handbooks on Information Systems, Springer, number 978-3-540-49487-4, November.
    14. Antonio Doria, Francisco, 2011. "J.B. Rosser Jr. , Handbook of Research on Complexity, Edward Elgar, Cheltenham, UK--Northampton, MA, USA (2009) 436 + viii pp., index, ISBN 978 1 84542 089 5 (cased)," Journal of Economic Behavior & Organization, Elsevier, vol. 78(1-2), pages 196-204, April.
    15. Alfarano, Simone & Lux, Thomas & Wagner, Friedrich, 2008. "Time variation of higher moments in a financial market with heterogeneous agents: An analytical approach," Journal of Economic Dynamics and Control, Elsevier, vol. 32(1), pages 101-136, January.
    16. Baosheng Yuan & Kan Chen, 2005. "Impact of Investor's Varying Risk Aversion on the Dynamics of Asset Price Fluctuations," Papers physics/0506224, arXiv.org.
    17. Sornette, Didier & Zhou, Wei-Xing, 2006. "Importance of positive feedbacks and overconfidence in a self-fulfilling Ising model of financial markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 370(2), pages 704-726.
    18. Staccioli, Jacopo & Napoletano, Mauro, 2021. "An agent-based model of intra-day financial markets dynamics," Journal of Economic Behavior & Organization, Elsevier, vol. 182(C), pages 331-348.
    19. Hommes, Cars H., 2006. "Heterogeneous Agent Models in Economics and Finance," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 23, pages 1109-1186, Elsevier.
    20. Johann Lussange & Ivan Lazarevich & Sacha Bourgeois-Gironde & Stefano Palminteri & Boris Gutkin, 2021. "Modelling Stock Markets by Multi-agent Reinforcement Learning," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 113-147, January.

    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:2111.11315. 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.