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The bounds of heavy-tailed return distributions in evolving complex networks

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  • Jo\~ao P. da Cruz
  • Pedro G. Lind
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    Abstract

    We consider the evolution of scale-free networks according to preferential attachment schemes and show the conditions for which the exponent characterizing the degree distribution is bounded by upper and lower values. Our framework is an agent model, presented in the context of economic networks of trades, which shows the emergence of critical behavior. Starting from a brief discussion about the main features of the evolving network of trades, we show that the logarithmic return distributions have bounded heavy-tails, and the corresponding bounding exponent values can be derived. Finally, we discuss these findings in the context of model risk.

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    File URL: http://arxiv.org/pdf/1109.2803
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    Bibliographic Info

    Paper provided by arXiv.org in its series Papers with number 1109.2803.

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    Date of creation: Sep 2011
    Date of revision: Jan 2013
    Publication status: Published in Physics Letters A,Vol.377,3-4,p189-194,(2013)
    Handle: RePEc:arx:papers:1109.2803

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    Web page: http://arxiv.org/

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    1. Manishi Prasad & Peter Wahlqvist & Rich Shikiar & Ya-Chen Tina Shih, 2004. "A," PharmacoEconomics, Springer Healthcare | Adis, Springer Healthcare | Adis, vol. 22(4), pages 225-244.
    2. Lux, T. & M. Marchesi, . "Volatility Clustering in Financial Markets: A Micro-Simulation of Interacting Agents," Discussion Paper Serie B 437, University of Bonn, Germany, revised Jul 1998.
    3. Levy, Moshe & Levy, Haim & Solomon, Sorin, 1994. "A microscopic model of the stock market : Cycles, booms, and crashes," Economics Letters, Elsevier, vol. 45(1), pages 103-111, May.
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