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Econophysics review: II. Agent-based models

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  • Anirban Chakraborti
  • Ioane Muni Toke
  • Marco Patriarca
  • Frederic Abergel
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    Abstract

    This article is the second part of a review of recent empirical and theoretical developments usually grouped under the heading Econophysics. In the first part, we reviewed the statistical properties of financial time series, the statistics exhibited in order books and discussed some studies of correlations of asset prices and returns. This second part deals with models in Econophysics from the point of view of agent-based modeling. Of the large number of multi-agent-based models, we have identified three representative areas. First, using previous work originally presented in the fields of behavioral finance and market microstructure theory, econophysicists have developed agent-based models of order-driven markets that we discuss extensively here. Second, kinetic theory models designed to explain certain empirical facts concerning wealth distribution are reviewed. Third, we briefly summarize game theory models by reviewing the now classic minority game and related problems.

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    File URL: http://www.tandfonline.com/doi/abs/10.1080/14697688.2010.539249
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    Bibliographic Info

    Article provided by Taylor & Francis Journals in its journal Quantitative Finance.

    Volume (Year): 11 (2011)
    Issue (Month): 7 ()
    Pages: 1013-1041

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    Handle: RePEc:taf:quantf:v:11:y:2011:i:7:p:1013-1041

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    Web page: http://www.tandfonline.com/RQUF20

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    Keywords: Econophysics; Financial time series; Correlation; Agent based modelling;

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    Cited by:
    1. Luisanna Cocco & Giulio Concas & Michele Marchesi, 2014. "Using an Artificial Financial Market for studying a Cryptocurrency Market," Papers 1406.6496, arXiv.org.
    2. Charles-Albert Lehalle, 2013. "Market Microstructure Knowledge Needed for Controlling an Intra-Day Trading Process," Papers 1302.4592, arXiv.org.
    3. Salvador Pueyo, 2013. "Is it a power law distribution? The case of economic contractions," Papers 1310.2567, arXiv.org.
    4. Jean-Philippe Bouchaud, 2012. "Crises and collective socio-economic phenomena: simple models and challenges," Papers 1209.0453, arXiv.org, revised Dec 2012.
    5. Seemann, Lars & Hua, Jia-Chen & McCauley, Joseph L. & Gunaratne, Gemunu H., 2012. "Ensemble vs. time averages in financial time series analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(23), pages 6024-6032.
    6. Li-Xin Wang, 2014. "Dynamical Models of Stock Prices Based on Technical Trading Rules Part II: Analysis of the Models," Papers 1401.1891, arXiv.org.
    7. Aleksejus Kononovicius & Valentas Daniunas, 2013. "Agent-based and macroscopic modeling of the complex socio-economic systems," Papers 1303.3693, arXiv.org, revised Apr 2013.
    8. Li-Xin Wang, 2014. "Gaussian-Chain Filters for Heavy-Tailed Noise with Application to Detecting Big Buyers and Big Sellers in Stock Market," Papers 1405.2220, arXiv.org.
    9. Tu, Chengyi, 2014. "Cointegration-based financial networks study in Chinese stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 402(C), pages 245-254.
    10. Drakopoulos, Stavros A. & Katselidis, Ioannis, 2013. "From Edgeworth to Econophysics: A Methodological Perspective," MPRA Paper 46975, University Library of Munich, Germany.
    11. Kirill Vaninsky & Stepan Myzuchka & Alexander Lukov, 2012. "A multi-agent nonlinear Markov model of the order book," Papers 1208.3083, arXiv.org, revised Dec 2013.

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