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Evolutionary financial market models

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  • Ponzi, A.
  • Aizawa, Y.

Abstract

We study computer simulations of two financial market models, the second a simplified model of the first. The first is a model of the self-organized formation and breakup of crowds of traders, motivated by the dynamics of competitive evolving systems which shows interesting self-organized critical (SOC)-type behaviour without any fine tuning of control parameters. This SOC-type avalanching and stasis appear as realistic volatility clustering in the price returns time series. The market becomes highly ordered at ‘crashes’ but gradually loses this order through randomization during the intervening stasis periods. The second model is a model of stocks interacting through a competitive evolutionary dynamic in a common stock exchange. This model shows a self-organized ‘market-confidence’. When this is high the market is stable but when it gets low the market may become highly volatile. Volatile bursts rapidly increase the market confidence again. This model shows a phase transition as temperature parameter is varied. The price returns time series in the transition region is very realistic power-law truncated Levy distribution with clustered volatility and volatility superdiffusion. This model also shows generally positive stock cross-correlations as is observed in real markets. This model may shed some light on why such phenomena are observed.

Suggested Citation

  • Ponzi, A. & Aizawa, Y., 2000. "Evolutionary financial market models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 287(3), pages 507-523.
    • Handle: RePEc:eee:phsmap:v:287:y:2000:i:3:p:507-523
    DOI: 10.1016/S0378-4371(00)00389-7
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    References listed on IDEAS

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    1. Vasiliki Plerou & Parameswaran Gopikrishnan & Bernd Rosenow & Luis A. Nunes Amaral & H. Eugene Stanley, 1999. "Universal and non-universal properties of cross-correlations in financial time series," Papers cond-mat/9902283, arXiv.org.
    2. Yanhui Liu & Parameswaran Gopikrishnan & Pierre Cizeau & Martin Meyer & Chung-Kang Peng & H. Eugene Stanley, 1999. "The statistical properties of the volatility of price fluctuations," Papers cond-mat/9903369, arXiv.org, revised Mar 1999.
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    Cited by:

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    2. Salvador Pueyo, 2014. "Ecological Econophysics for Degrowth," Sustainability, MDPI, vol. 6(6), pages 1-53, May.
    3. Beyeler, Walter E. & Glass, Robert J. & Bech, Morten L. & Soramäki, Kimmo, 2007. "Congestion and cascades in payment systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 384(2), pages 693-718.
    4. Wu, Jinshan & Di, Zengru & Yang, Zhanru, 2003. "Division of labor as the result of phase transition," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 323(C), pages 663-676.
    5. R. D. Groot, 2004. "Levy distribution and long correlation times in supermarket sales," Papers cond-mat/0412163, arXiv.org.
    6. Sun-Chong Wang & Sai-Ping Li & Chung-Ching Tai & Shu-Heng Che, 2009. "Statistical properties of an experimental political futures market," Quantitative Finance, Taylor & Francis Journals, vol. 9(1), pages 9-16.
    7. Jovanovic, Franck & Schinckus, Christophe, 2017. "Econophysics and Financial Economics: An Emerging Dialogue," OUP Catalogue, Oxford University Press, number 9780190205034.

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