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On the problem of calibrating an agent based model for financial markets

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  • Annalisa Fabretti

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Abstract

Agent based models are very widely used in different disciplines. In financial markets, they can be used to explain well known features called stylised facts and fit statistical properties of data. For this reason, they can model price movements better than standard models using gaussianity. Calibration and validation are essential issues in agent-based modeling. However, calibrating such models is not yet sufficiently considered in the literature. In this paper, a Nelder–Mead simplex algorithm coupled with threshold accepting algorithm (Gilli and Winker in Comput Stat Data Anal 42:299–312, 2003 ) and a genetic algorithm have been implemented to calibrate the model presented by Farmer and Joshi (J Econ Behav Org 49:149–171, 2002 ) and the outcomes have been compared and discussed. The data used are closing prices of S&P500 Composite index and a particular attention has been devoted to the choice of the objective function. Copyright Springer-Verlag 2013

Suggested Citation

  • Annalisa Fabretti, 2013. "On the problem of calibrating an agent based model for financial markets," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 8(2), pages 277-293, October.
    • Handle: RePEc:spr:jeicoo:v:8:y:2013:i:2:p:277-293
    DOI: 10.1007/s11403-012-0096-3
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    References listed on IDEAS

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    Cited by:

    1. Lamperti, Francesco & Roventini, Andrea & Sani, Amir, 2018. "Agent-based model calibration using machine learning surrogates," Journal of Economic Dynamics and Control, Elsevier, vol. 90(C), pages 366-389.
    2. Platt, Donovan, 2020. "A comparison of economic agent-based model calibration methods," Journal of Economic Dynamics and Control, Elsevier, vol. 113(C).
    3. Giorgio Fagiolo & Andrea Roventini, 2017. "Macroeconomic Policy in DSGE and Agent-Based Models Redux: New Developments and Challenges Ahead," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 20(1), pages 1-1.
    4. Mark N Read & Jacqueline Bailey & Jon Timmis & Tatyana Chtanova, 2016. "Leukocyte Motility Models Assessed through Simulation and Multi-objective Optimization-Based Model Selection," PLOS Computational Biology, Public Library of Science, vol. 12(9), pages 1-34, September.
    5. Lamperti, Francesco & Roventini, Andrea & Sani, Amir, 2018. "Agent-based model calibration using machine learning surrogates," Journal of Economic Dynamics and Control, Elsevier, vol. 90(C), pages 366-389.
    6. Grazzini, Jakob & Richiardi, Matteo G. & Tsionas, Mike, 2017. "Bayesian estimation of agent-based models," Journal of Economic Dynamics and Control, Elsevier, vol. 77(C), pages 26-47.
    7. Francesco Lamperti, 2016. "Empirical Validation of Simulated Models through the GSL-div: an Illustrative Application," LEM Papers Series 2016/18, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    8. Nan Lu, 2018. "La modélisation de l’indice CAC 40 avec un modèle basé agent," Erudite Ph.D Dissertations, Erudite, number ph18-02 edited by François Legendre, May.
    9. Bàrbara Llacay & Gilbert Peffer, 2018. "Using realistic trading strategies in an agent-based stock market model," Computational and Mathematical Organization Theory, Springer, vol. 24(3), pages 308-350, September.
    10. Alexandru Mandes & Peter Winker, 2017. "Complexity and model comparison in agent based modeling of financial markets," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 12(3), pages 469-506, October.
    11. Giorgio Fagiolo & Mattia Guerini & Francesco Lamperti & Alessio Moneta & Andrea Roventini, 2017. "Validation of Agent-Based Models in Economics and Finance," LEM Papers Series 2017/23, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    12. Mohammad Ghaderi, 2020. "Public Health Interventions in the Face of Pandemics: Network Structure, Social Distancing, and Heterogeneity," Working Papers 1193, Barcelona Graduate School of Economics.
    13. Donovan Platt, 2019. "A Comparison of Economic Agent-Based Model Calibration Methods," Papers 1902.05938, arXiv.org.
    14. 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.
    15. Platt, Donovan & Gebbie, Tim, 2018. "Can agent-based models probe market microstructure?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 1092-1106.
    16. Grazzini, Jakob & Richiardi, Matteo, 2015. "Estimation of ergodic agent-based models by simulated minimum distance," Journal of Economic Dynamics and Control, Elsevier, vol. 51(C), pages 148-165.
    17. Lamperti, Francesco, 2018. "An information theoretic criterion for empirical validation of simulation models," Econometrics and Statistics, Elsevier, vol. 5(C), pages 83-106.
    18. Jakob Grazzini & Matteo G. Richiardi, 2013. "Consistent Estimation of Agent-Based Models by Simulated Minimum Distance," LABORatorio R. Revelli Working Papers Series 130, LABORatorio R. Revelli, Centre for Employment Studies.
    19. Mohammad Ghaderi, 2020. "Public health interventions in the face of pandemics: network structure, social distancing, and heterogeneity," Economics Working Papers 1732, Department of Economics and Business, Universitat Pompeu Fabra.

    More about this item

    Keywords

    Agent based model; Artificial markets; Calibration; Genetic algorithm; C13; C52; G10;

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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