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Using an artificial financial market for studying a cryptocurrency market

Author

Listed:
  • Luisanna Cocco

    (University of Cagliari)

  • Giulio Concas

    (University of Cagliari)

  • Michele Marchesi

    (University of Cagliari)

Abstract

This paper presents an agent-based artificial cryptocurrency market in which heterogeneous agents buy or sell cryptocurrencies, in particular Bitcoins. In this market, there are two typologies of agents, Random Traders and Chartists, which interact with each other by trading Bitcoins. Each agent is initially endowed with a finite amount of crypto and/or fiat cash and issues buy and sell orders, according to her strategy and resources. The number of Bitcoins increases over time with a rate proportional to the real one, even if the mining process is not explicitly modelled. The model proposed is able to reproduce some of the real statistical properties of the price returns observed in the Bitcoin real market. In particular, it is able to reproduce the unit root property, the fat tail phenomenon and the volatility clustering. The simulator has been implemented using object-oriented technology, and could be considered a valid starting point to study and analyse the cryptocurrency market and its future evolutions.

Suggested Citation

  • Luisanna Cocco & Giulio Concas & Michele Marchesi, 2017. "Using an artificial financial market for studying a cryptocurrency market," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 12(2), pages 345-365, July.
    • Handle: RePEc:spr:jeicoo:v:12:y:2017:i:2:d:10.1007_s11403-015-0168-2
    DOI: 10.1007/s11403-015-0168-2
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    2. Fabio Della Rossa & Lorenzo Giannini & Pietro DeLellis, 2020. "Herding or wisdom of the crowd? Controlling efficiency in a partially rational financial market," PLOS ONE, Public Library of Science, vol. 15(9), pages 1-16, September.
    3. Emilio Abad-Segura & Alfonso Infante-Moro & Mariana-Daniela González-Zamar & Eloy López-Meneses, 2021. "Blockchain Technology for Secure Accounting Management: Research Trends Analysis," Mathematics, MDPI, vol. 9(14), pages 1-26, July.
    4. Sandip Mukherji, 2019. "Empirical Evidence On Bitcoin Returns And Portfolio Value," The International Journal of Business and Finance Research, The Institute for Business and Finance Research, vol. 13(2), pages 71-81.
    5. Zhang, Xin & Yang, Liansheng & Zhu, Yingming, 2019. "Analysis of multifractal characterization of Bitcoin market based on multifractal detrended fluctuation analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 973-983.
    6. Johann Lussange & Stefano Vrizzi & Stefano Palminteri & Boris Gutkin, 2024. "Modelling crypto markets by multi-agent reinforcement learning," Papers 2402.10803, arXiv.org.
    7. Alberto Ciacci & Takumi Sueshige & Hideki Takayasu & Kim Christensen & Misako Takayasu, 2020. "The microscopic relationships between triangular arbitrage and cross-currency correlations in a simple agent based model of foreign exchange markets," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-19, June.
    8. Kononovicius, Aleksejus & Ruseckas, Julius, 2019. "Order book model with herd behavior exhibiting long-range memory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 171-191.
    9. Fan Fang & Carmine Ventre & Michail Basios & Leslie Kanthan & David Martinez-Rego & Fan Wu & Lingbo Li, 2022. "Cryptocurrency trading: a comprehensive survey," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-59, December.
    10. Silvia Bartolucci & Andrei Kirilenko, 2019. "A Model of the Optimal Selection of Crypto Assets," Papers 1906.09632, arXiv.org.
    11. Zura Kakushadze & Jim Kyung-Soo Liew, 2018. "CryptoRuble: From Russia with Love," Papers 1801.05760, arXiv.org.
    12. Fan Fang & Carmine Ventre & Michail Basios & Leslie Kanthan & Lingbo Li & David Martinez-Regoband & Fan Wu, 2020. "Cryptocurrency Trading: A Comprehensive Survey," Papers 2003.11352, arXiv.org, revised Jan 2022.
    13. Vladimir Soloviev & Andrey Belinskiy, 2018. "Methods of nonlinear dynamics and the construction of cryptocurrency crisis phenomena precursors," Papers 1807.05837, arXiv.org, revised Jul 2018.
    14. Rico-Peña, Juan Jesús & Arguedas-Sanz, Raquel & López-Martin, Carmen, 2023. "Models used to characterise blockchain features. A systematic literature review and bibliometric analysis," Technovation, Elsevier, vol. 123(C).
    15. Aleksejus Kononovicius & Vygintas Gontis, 2019. "Approximation of the first passage time distribution for the birth-death processes," Papers 1902.00924, arXiv.org.
    16. Silvia Bartolucci & Fabio Caccioli & Pierpaolo Vivo, 2019. "A percolation model for the emergence of the Bitcoin Lightning Network," Papers 1912.03556, arXiv.org.
    17. Adão, Luiz F.S. & Silveira, Douglas & Ely, Regis A. & Cajueiro, Daniel O., 2022. "The impacts of interest rates on banks’ loan portfolio risk-taking," Journal of Economic Dynamics and Control, Elsevier, vol. 144(C).
    18. Pengfei Wang & Wei Zhang & Xiao Li & Dehua Shen, 2019. "Trading volume and return volatility of Bitcoin market: evidence for the sequential information arrival hypothesis," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 14(2), pages 377-418, June.
    19. Chu, Jeffrey & Chan, Stephen & Zhang, Yuanyuan, 2020. "High frequency momentum trading with cryptocurrencies," Research in International Business and Finance, Elsevier, vol. 52(C).
    20. Klarin, Anton, 2020. "The decade-long cryptocurrencies and the blockchain rollercoaster: Mapping the intellectual structure and charting future directions," Research in International Business and Finance, Elsevier, vol. 51(C).

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