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Why a simple herding model may generate the stylized facts of daily returns: explanation and estimation

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  • Reiner Franke

    () (University of Kiel)

  • Frank Westerhoff

    (University of Bamberg)

Abstract

Abstract The paper proposes an elementary agent-based asset pricing model that, invoking the two trader types of fundamentalists and chartists, comprises four features: (i) price determination by excess demand; (ii) a herding mechanism that gives rise to a macroscopic adjustment equation for the market fractions of the two groups; (iii) a rush towards fundamentalism when the price misalignment becomes too large; and (iv) a stronger noise component in the demand per chartist trader than in the demand per fundamentalist trader, which implies a structural stochastic volatility in the returns. Combining analytical and numerical methods, the interaction between these elements is studied in the phase plane of the price and a majority index. In addition, the model is estimated by the method of simulated moments, where the choice of the moments reflects the basic stylized facts of the daily returns of a stock market index. A (parametric) bootstrap procedure serves to set up an econometric test to evaluate the model’s goodness-of-fit, which proves to be highly satisfactory. The bootstrap also makes sure that the estimated structural parameters are well identified.

Suggested Citation

  • Reiner Franke & Frank Westerhoff, 2016. "Why a simple herding model may generate the stylized facts of daily returns: explanation and estimation," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 11(1), pages 1-34, April.
    • Handle: RePEc:spr:jeicoo:v:11:y:2016:i:1:d:10.1007_s11403-014-0140-6
    DOI: 10.1007/s11403-014-0140-6
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    Cited by:

    1. Schmitt, Noemi & Westerhoff, Frank, 2017. "On the bimodality of the distribution of the S&P 500's distortion: Empirical evidence and theoretical explanations," Journal of Economic Dynamics and Control, Elsevier, vol. 80(C), pages 34-53.
    2. repec:eee:dyncon:v:85:y:2017:i:c:p:21-45 is not listed on IDEAS
    3. Gaffeo, Edoardo & Molinari, Massimo, 2017. "Taxing financial transactions in fundamentally heterogeneous markets," Economic Modelling, Elsevier, vol. 64(C), pages 322-333.
    4. repec:eee:ecosta:v:5:y:2018:i:c:p:83-106 is not listed on IDEAS
    5. Kukacka, Jiri & Barunik, Jozef, 2017. "Estimation of financial agent-based models with simulated maximum likelihood," Journal of Economic Dynamics and Control, Elsevier, vol. 85(C), pages 21-45.
    6. Barde, Sylvain, 2016. "Direct comparison of agent-based models of herding in financial markets," Journal of Economic Dynamics and Control, Elsevier, vol. 73(C), pages 329-353.
    7. repec:eee:dyncon:v:91:y:2018:i:c:p:391-408 is not listed on IDEAS
    8. Adam Majewski & Stefano Ciliberti & Jean-Philippe Bouchaud, 2018. "Co-existence of Trend and Value in Financial Markets: Estimating an Extended Chiarella Model," Papers 1807.11751, arXiv.org.
    9. Fabio Tramontana, 2013. "The role of cognitively biased imitators in a small scale agent-based financial market," DEM Working Papers Series 029, University of Pavia, Department of Economics and Management.

    More about this item

    Keywords

    Structural stochastic volatility; Method of simulated moments; Autocorrelation pattern; Fat tails; Bootstrapped $$p$$ p values;

    JEL classification:

    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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