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Asset price dynamics with small world interactions under hetereogeneous beliefs

Author

Listed:
  • Valentyn Panchenko

    (School of Economics, University of New South Wales)

  • Sergiy Gerasymchuk

    (Advanced School of Economics, University of Venice)

  • Oleg V. Pavlov

    (Department of Social Science and Policy Studies, Worchester Polytechnic Institute)

Abstract

We propose a simple model of a financial market populated with heterogeneous agents. The market represents a network with nodes symbolizing the agents and edges standing for connections between them, thus, embodying local interactions in the market. By local interactions we mean any kind of interplay between the decisions of the agents unaffected by the market mechanism and unrelated to the physical distance between the agents. Using the rewiring procedure we restructure a network from regular lattice to random graph by varying the probability of the agents to switch from one trading strategy to another. We study how the network structure influences the asset price dynamics. The results show that for some intermediate values of the probability to switch, corresponding to a small world network, the price dynamics become reminiscent to the real. While for the boundary values of the probability the dynamics lacks some typical features of the real financial markets.

Suggested Citation

  • Valentyn Panchenko & Sergiy Gerasymchuk & Oleg V. Pavlov, 2007. "Asset price dynamics with small world interactions under hetereogeneous beliefs," Working Papers 149, Department of Applied Mathematics, Università Ca' Foscari Venezia.
    • Handle: RePEc:vnm:wpaper:149
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    Cited by:

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    2. Sergiy Gerasymchuk, 2008. "Asset return and wealth dynamics with reference dependent preferences and heterogeneous beliefs," Working Papers 160, Department of Applied Mathematics, Università Ca' Foscari Venezia.
    3. De Kamps, Marc & Ladley, Daniel & Simaitis, Aistis, 2014. "Heterogeneous beliefs in over-the-counter markets," Journal of Economic Dynamics and Control, Elsevier, vol. 41(C), pages 50-68.
    4. Ibrahim Filiz & Thomas Nahmer & Markus Spiwoks & Kilian Bizer, 2018. "Portfolio diversification: the influence of herding, status-quo bias, and the gambler’s fallacy," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 32(2), pages 167-205, May.
    5. Gabriele Tedeschi & Stefania Vitali & Mauro Gallegati, 2014. "The dynamic of innovation networks: a switching model on technological change," Journal of Evolutionary Economics, Springer, vol. 24(4), pages 817-834, September.
    6. Chang Sheng-Kai, 2014. "Herd behavior, bubbles and social interactions in financial markets," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 18(1), pages 89-101, February.

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    More about this item

    Keywords

    local interactions; networks; small world; heterogeneous beliefs; price dynamics; bifurcations; chaos;
    All these keywords.

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C62 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Existence and Stability Conditions of Equilibrium
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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