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Direct calibration and comparison of agent-based herding models of financial markets

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  • Sylvain Barde

Abstract

The present paper aims to test a new model comparison methodology by calibrating and comparing three agent-based models of financial markets on the daily returns of 18 indices. The models chosen for this empirical application are the herding model of Gilli & Winker, its asymmetric version by Alfarano, Lux & Wagner and the more recent model by Franke & Westerhoff, which all share a common lineage to the herding model introduced by Kirman (1993). In addition, standard ARCH processes are included for each financial series to provide a benchmark for the explanatory power of the models. The methodology provides a clear and consistent ranking of the three models. More importantly, it also reveals that the best performing model, Franke & Westerhoff, is generally not distinguishable from an ARCH-type process, suggesting their explanatory power on the data is similar.

Suggested Citation

  • Sylvain Barde, 2015. "Direct calibration and comparison of agent-based herding models of financial markets," Studies in Economics 1507, School of Economics, University of Kent.
    • Handle: RePEc:ukc:ukcedp:1507
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    References listed on IDEAS

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    1. Tesfatsion, Leigh & Judd, Kenneth L., 2006. "Handbook of Computational Economics, Vol. 2: Agent-Based Computational Economics," Staff General Research Papers Archive 10368, Iowa State University, Department of Economics.
    2. Tesfatsion, Leigh, 2006. "Agent-Based Computational Economics: A Constructive Approach to Economic Theory," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 16, pages 831-880, Elsevier.
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    7. Sylvain Barde, 2015. "A Practical, Universal, Information Criterion over Nth Order Markov Processes," Studies in Economics 1504, School of Economics, University of Kent.
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    9. Hommes, Cars H., 2006. "Heterogeneous Agent Models in Economics and Finance," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 23, pages 1109-1186, Elsevier.
    10. Andrew Patton & Dimitris Politis & Halbert White, 2009. "Correction to “Automatic Block-Length Selection for the Dependent Bootstrap” by D. Politis and H. White," Econometric Reviews, Taylor & Francis Journals, vol. 28(4), pages 372-375.
    11. 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.
    12. Gilli, M. & Winker, P., 2003. "A global optimization heuristic for estimating agent based models," Computational Statistics & Data Analysis, Elsevier, vol. 42(3), pages 299-312, March.
    13. Francesco Lamperti, 2015. "An Information Theoretic Criterion for Empirical Validation of Time Series Models," LEM Papers Series 2015/02, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
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    Citations

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    Cited by:

    1. Donovan Platt & Tim Gebbie, 2016. "The Problem of Calibrating an Agent-Based Model of High-Frequency Trading," Papers 1606.01495, arXiv.org, revised Mar 2017.
    2. Johann Lussange & Stefano Vrizzi & Stefano Palminteri & Boris Gutkin, 2024. "Mesoscale effects of trader learning behaviors in financial markets: A multi-agent reinforcement learning study," PLOS ONE, Public Library of Science, vol. 19(4), pages 1-40, April.
    3. Radu T. Pruna & Maria Polukarov & Nicholas R. Jennings, 2020. "Loss aversion in an agent-based asset pricing model," Quantitative Finance, Taylor & Francis Journals, vol. 20(2), pages 275-290, February.
    4. Guerini, Mattia & Moneta, Alessio, 2017. "A method for agent-based models validation," Journal of Economic Dynamics and Control, Elsevier, vol. 82(C), pages 125-141.
    5. Donovan Platt & Tim Gebbie, 2016. "Can Agent-Based Models Probe Market Microstructure?," Papers 1611.08510, arXiv.org, revised Aug 2017.
    6. Pruna, Radu T. & Polukarov, Maria & Jennings, Nicholas R., 2018. "Avoiding regret in an agent-based asset pricing model," Finance Research Letters, Elsevier, vol. 24(C), pages 273-277.
    7. Zhenxi Chen & Thomas Lux, 2018. "Estimation of Sentiment Effects in Financial Markets: A Simulated Method of Moments Approach," Computational Economics, Springer;Society for Computational Economics, vol. 52(3), pages 711-744, October.
    8. Johann Lussange & Stefano Vrizzi & Stefano Palminteri & Boris Gutkin, 2024. "Mesoscale effects of trader learning behaviors in financial markets: A multi-agent reinforcement learning study," Post-Print hal-04790290, HAL.
    9. Radu T. Pruna & Maria Polukarov & Nicholas R. Jennings, 2016. "A new structural stochastic volatility model of asset pricing and its stylized facts," Papers 1604.08824, arXiv.org.

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    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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