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Informative Contagion Dynamics in a Multilayer Network Model of Financial Markets

Author

Listed:
  • Alessio Emanuele Biondo

    (Università degli Studi di Catania)

  • Alessandro Pluchino

    (Università degli Studi di Catania)

  • Andrea Rapisarda

    (Università degli Studi di Catania)

Abstract

In this paper we present a multilayer network model with contagion dynamics which is able to simulate the spreading of information and the transactions phase of a typical financial market. A rudimental order book dynamics is embedded in a framework where the trading decisions of investors and the information dynamics occur in two separated layers with different network topologies. The analysis addresses and compares the behaviour of an isolated one-asset market and a corresponding two-assets version, with different correlation degrees. Despite some simplifying assumptions, results show compliance to stylized facts exhibited by density functions of true financial returns.

Suggested Citation

  • Alessio Emanuele Biondo & Alessandro Pluchino & Andrea Rapisarda, 2017. "Informative Contagion Dynamics in a Multilayer Network Model of Financial Markets," Italian Economic Journal: A Continuation of Rivista Italiana degli Economisti and Giornale degli Economisti, Springer;Società Italiana degli Economisti (Italian Economic Association), vol. 3(3), pages 343-366, November.
    • Handle: RePEc:spr:italej:v:3:y:2017:i:3:d:10.1007_s40797-017-0052-4
    DOI: 10.1007/s40797-017-0052-4
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    Cited by:

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    2. L. S. Di Mauro & A. Pluchino & A. E. Biondo, 2018. "A Game of Tax Evasion: evidences from an agent-based model," Papers 1809.08146, arXiv.org.
    3. Alessio Emanuele Biondo, 2018. "Order book microstructure and policies for financial stability," Studies in Economics and Finance, Emerald Group Publishing Limited, vol. 35(1), pages 196-218, March.
    4. Biondo, A.E. & Pluchino, A. & Rapisarda, A., 2018. "Modeling surveys effects in political competitions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 714-726.
    5. Biondo, Alessio Emanuele, 2017. "Learning to forecast, risk aversion, and microstructural aspects of financial stability," Economics Discussion Papers 2017-104, Kiel Institute for the World Economy (IfW Kiel).
    6. Biondo, Alessio Emanuele, 2018. "Learning to forecast, risk aversion, and microstructural aspects of financial stability," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 12, pages 1-21.
    7. Zhang, Ronda J. & Ye, Fred Y., 2020. "Measuring similarity for clarifying layer difference in multiplex ad hoc duplex information networks," Journal of Informetrics, Elsevier, vol. 14(1).
    8. Huang, Qi-An & Zhao, Jun-Chan & Wu, Xiao-Qun, 2022. "Financial risk propagation between Chinese and American stock markets based on multilayer networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 586(C).
    9. Simpson, Jesse R. & Mishra, Sabyasachee, 2021. "Developing a methodology to predict the adoption rate of Connected Autonomous Trucks in transportation organizations using peer effects," Research in Transportation Economics, Elsevier, vol. 90(C).

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    More about this item

    Keywords

    Financial market; Self organized criticality; Multilayer networks; Agent-based models; Informative contagion;
    All these keywords.

    JEL classification:

    • G1 - Financial Economics - - General Financial Markets
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • C40 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - General

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