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Network hierarchy in Kirman's ant model: fund investment can create systemic risk

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  • Alfarano, Simone
  • Milaković, Mishael
  • Raddant, Matthias

Abstract

Kirman's ant model has been used to characterize the expectation formation of financial investors who are prone to herding. The model's original version suffers from the problem of N-dependence: its ability to replicate the statistical features of financial returns vanishes once the system size N is increased. In a generalized version of the ant model, the network structure connecting agents turns out to determine whether or not the model is N-dependent. We investigate a class of hierarchical networks in the generalized model that presumably reflect the institutional heterogeneity of financial markets. These network structures do overcome the problem of N-dependence, but at the same time they also increase system-wide volatility. Thus network structure becomes an auxiliary source of volatility in addition to the behavioral heterogeneity of interacting agents.

Suggested Citation

  • Alfarano, Simone & Milaković, Mishael & Raddant, Matthias, 2009. "Network hierarchy in Kirman's ant model: fund investment can create systemic risk," Economics Working Papers 2009-09, Christian-Albrechts-University of Kiel, Department of Economics.
    • Handle: RePEc:zbw:cauewp:200909
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    References listed on IDEAS

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    1. Aoki, Masanao, 2008. "Thermodynamic limits of macroeconomic or financial models: One- and two-parameter Poisson-Dirichlet models," Journal of Economic Dynamics and Control, Elsevier, vol. 32(1), pages 66-84, January.
    2. Lux, Thomas & Schornstein, Sascha, 2005. "Genetic learning as an explanation of stylized facts of foreign exchange markets," Journal of Mathematical Economics, Elsevier, vol. 41(1-2), pages 169-196, February.
    3. Alan Kirman, 1993. "Ants, Rationality, and Recruitment," The Quarterly Journal of Economics, Oxford University Press, vol. 108(1), pages 137-156.
    4. Alfarano, Simone & Lux, Thomas & Wagner, Friedrich, 2008. "Time variation of higher moments in a financial market with heterogeneous agents: An analytical approach," Journal of Economic Dynamics and Control, Elsevier, vol. 32(1), pages 101-136, January.
    5. Russ Wermers, 1999. "Mutual Fund Herding and the Impact on Stock Prices," Journal of Finance, American Finance Association, vol. 54(2), pages 581-622, April.
    6. Harrison Hong & Jeffrey D. Kubik & Jeremy C. Stein, 2005. "Thy Neighbor's Portfolio: Word‐of‐Mouth Effects in the Holdings and Trades of Money Managers," Journal of Finance, American Finance Association, vol. 60(6), pages 2801-2824, December.
    7. Simone Alfarano & Thomas Lux & Friedrich Wagner, 2005. "Estimation of Agent-Based Models: The Case of an Asymmetric Herding Model," Computational Economics, Springer;Society for Computational Economics, vol. 26(1), pages 19-49, August.
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    Cited by:

    1. Albrecht Irle & Jonas Kauschke & Thomas Lux & Mishael Milaković, 2011. "Switching Rates And The Asymptotic Behavior Of Herding Models," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 14(03), pages 359-376.
    2. Chang, Chia-ling & Chen, Shu-heng, 2011. "Interactions in DSGE models: The Boltzmann-Gibbs machine and social networks approach," Economics Discussion Papers 2011-25, Kiel Institute for the World Economy (IfW).
    3. Chen, Shu-heng & Chang, Chia-ling, 2012. "Interactions in the New Keynesian DSGE models: The Boltzmann-Gibbs machine and social networks approach," Economics - The Open-Access, Open-Assessment E-Journal, Kiel Institute for the World Economy (IfW), vol. 6, pages 1-32.

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    Keywords

    herding; financial markets; networks; N-dependence; systemic risk;

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