Using an artificial financial market for studying a cryptocurrency market

Regular Article

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.

Keywords

Artificial financial market Cryptocurrency Bitcoin Heterogeneous agents Market simulation 

References

  1. Androulaki E, Karame G, Roeschlin M, Scherer T, Capkun S (2013) Evaluating user privacy in Bitcoin. In: Proceedings of the financial cryptography and data security conference (FC)Google Scholar
  2. Bae KH, Jang H, Park KS (2003) Traders choice between limit and market orders: evidence from NYSE stocks. J Financ Mark 6:517–538CrossRefGoogle Scholar
  3. Bergstra JA, Leeuw DK (2013) Questions related to Bitcoin and other informational money. CoRR 1305:5956Google Scholar
  4. Bornholdt S, Sneppen K (2014) Do Bitcoins make the world go round? On the dynamics of competing crypto-currencies. CoRR.arXiv:1403.6378
  5. Brezo F, Bringas PG (2012) Issues and risks associated with cryptocurrencies such as Bitcoin. The second international conference on social eco-informaticsGoogle Scholar
  6. Chakraborti A, Toke IM, Patriarca M, Abergel F (2011) Econophysics review: II. Agent-based models. Quant Finance 11(7):1013–1041CrossRefGoogle Scholar
  7. Cincotti S, Focardi S, Marchesi M, Raberto M (2003) Who wins? Study of long-run trader survival in an artificial stock market. Phys A 324(1):227–233CrossRefGoogle Scholar
  8. Eyal I, Sirer E (2013) Majority is not enough: Bitcoin mining is vulnerable. CoRR 1311:0243Google Scholar
  9. Hanley BP (2013) The False premises and promises of Bitcoin. CoRR.arXiv:1312.2048
  10. Hommes CH (2006) Heterogeneous agent models in economics and finance. In: Handbook of computational economics, Agent-based computational economics, vol 2. Elsevier, pp 1109–1186Google Scholar
  11. Hout MCV, Bingham T (2014) Responsible vendors, intelligent consumers: Silk Road, the online revolution in drug trading. Int J Drug Policy 25:183–189CrossRefGoogle Scholar
  12. LeBaron B (2006) Agent-based computational finance. In: Handbook of computational economics, Agent-based computational economics, vol 2. Elsevier, pp 1187–1233Google Scholar
  13. Levy M, Solomon S (1997) New evidence in the power-law distribution of wealth. Phys A 242(12):9094Google Scholar
  14. Liua X, Gregorc S, Yang J (2008) The effects of behavioral and structural assumptions in artificial stock market. Phys A 387:25352546Google Scholar
  15. Luthe W (2013) Cryptocurrencies, network effects, and switching costs. Mercatus Center working paper no. 13-17Google Scholar
  16. Lux T, Marchesi M (2000) Volatility clustering in financial markets: a microsimulation of interacting agents. Int J Theor Appl Finance 3(4):675–702CrossRefGoogle Scholar
  17. Mike S, Farmer JD (2008) An empirical behavioral model of liquidity and volatility. J Econ Dyn Control 32:200234CrossRefGoogle Scholar
  18. Moore T (2013) The promise and perils of digital currencies. Int J Crit Infrastruct Protect Agent-Based Comput Econ 6(3–4):147–149Google Scholar
  19. Nakamoto S (2009) Bitcoin: a peer-to-peer electronic cash system. www.Bitcoin.org
  20. Newman MEJ (2005) Power laws. Pareto distributions and Zipf’s law. Contemp Phys 46(5):323–351CrossRefGoogle Scholar
  21. Pagan A (1996) The econometrics of financial markets. J Empirical Finance 3:15–102CrossRefGoogle Scholar
  22. Ponta L, Scalas E, Raberto M, Cincotti S (2012) Statistical analysis and agent-based microstructure modeling of high-frequency financial trading. IEEE J Select Topics Signal Process 6(4):381–387Google Scholar
  23. Raberto M, Cincotti S, Focardi S, Marchesi M (2001) Agent-based simulation of a financial market. Phys A 299(1):319–327CrossRefGoogle Scholar
  24. Raberto M, Cincotti S, Dose C, Focardi S, Marchesi M (2005) Price formation in an artificial market: limit order book versus matching of supply and demand. Nonlinear dynamics and heterogenous interacting agents. Springer, BerlinGoogle Scholar
  25. Singh P, Chandavarkar BR, Arora S, Agrawal N (2013) Performance comparison of executing fast transactions in bitcoin network using verifiable code execution. Second international conference on advanced computing, networking and securityGoogle Scholar
  26. Takayasu H (1990) Fractals in the physical sciences. Wiley, New YorkGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Luisanna Cocco
    • 1
  • Giulio Concas
    • 1
  • Michele Marchesi
    • 1
  1. 1.Department of Electrical and Electronic EngineeringUniversity of CagliariCagliariItaly

Personalised recommendations