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A minimal noise trader model with realistic time series

Author

Listed:
  • Simone Alfarano
  • Thomas Lux

Abstract

No abstract is available for this item.

Suggested Citation

  • Simone Alfarano & Thomas Lux, 2002. "A minimal noise trader model with realistic time series," Computing in Economics and Finance 2002 317, Society for Computational Economics.
    • Handle: RePEc:sce:scecf2:317
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    Citations

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    Cited by:

    1. David Morton de Lachapelle & Damien Challet, 2009. "Turnover, account value and diversification of real traders: evidence of collective portfolio optimizing behavior," Papers 0912.4723, arXiv.org, revised Jun 2010.
    2. Anne Peguin-Feissolle & Gilles Dufrénot & Dominique Guegan, 2006. "Changing-regime volatility : A fractionally integrated SETAR model," Working Papers halshs-00410540, HAL.
    3. Alfarano, Simone & Lux, Thomas, 2007. "A Noise Trader Model As A Generator Of Apparent Financial Power Laws And Long Memory," Macroeconomic Dynamics, Cambridge University Press, vol. 11(S1), pages 80-101, November.
    4. 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.
    5. Heni Boubaker & Nadia Sghaier, 2014. "Wavelet based Estimation of Time- Varying Long Memory Model with Nonlinear Fractional Integration Parameter," Working Papers 2014-284, Department of Research, Ipag Business School.
    6. Alfarano, Simone & Lux, Thomas & Wagner, Friedrich, 2010. "Excess Volatility and Herding in an Artificial Financial Market: Analytical Approach and Estimation," MPRA Paper 24719, University Library of Munich, Germany.

    More about this item

    Keywords

    Herd behaviour; speculative dynamics; fat tails; volatility clustering;
    All these keywords.

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

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