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Do ‘complex’ financial models really lead to complex dynamics? Agent-based models and multifractality

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  • Kukacka, Jiri
  • Kristoufek, Ladislav

Abstract

Agent-based models are usually claimed to generate complex dynamics; however, the link to such complexity has not been subject to rigorous examination. This paper studies this link between the complexity of financial time series—measured by their multifractal properties—and the design of various small-scale agent-based frameworks used to model the heterogeneity of financial markets. Nine popular models are analyzed, and while some of the models do not generate interesting multifractal patterns, we observe the strongest tendency towards multifractal behavior for the Bornholdt Ising model, the discrete choice-based models by Gaunersdorfer & Hommes and Schmitt & Westerhoff, and the transition probabilities-based framework by Franke & Westerhoff. Complexity is thus not an automatic feature of the time series generated by any agent-based model but generated only by models with specific properties. In addition, because multifractality is considered a financial stylized fact, its presence can be used as a new means to validate such models.

Suggested Citation

  • Kukacka, Jiri & Kristoufek, Ladislav, 2020. "Do ‘complex’ financial models really lead to complex dynamics? Agent-based models and multifractality," Journal of Economic Dynamics and Control, Elsevier, vol. 113(C).
    • Handle: RePEc:eee:dyncon:v:113:y:2020:i:c:s0165188920300257
    DOI: 10.1016/j.jedc.2020.103855
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    More about this item

    Keywords

    Complex systems; Financial agent-based models; Time series analysis; Multifractal analysis; Detrended fluctuation analysis;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
    • G02 - Financial Economics - - General - - - Behavioral Finance: Underlying Principles
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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