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An agent-based model of intra-day financial markets dynamics

Author

Listed:
  • Jacopo Staccioli

    (Unicatt - Università cattolica del Sacro Cuore [Milano], SSSUP - Scuola Universitaria Superiore Sant'Anna = Sant'Anna School of Advanced Studies [Pisa])

  • Mauro Napoletano

    (GREDEG - Groupe de Recherche en Droit, Economie et Gestion - UNS - Université Nice Sophia Antipolis (1965 - 2019) - CNRS - Centre National de la Recherche Scientifique - UniCA - Université Côte d'Azur, OFCE - Observatoire français des conjonctures économiques (Sciences Po) - Sciences Po - Sciences Po, SKEMA Business School, SSSUP - Scuola Universitaria Superiore Sant'Anna = Sant'Anna School of Advanced Studies [Pisa])

Abstract

We develop an agent-based model of a financial market which is able to jointly reproduce many of the stylised facts at different time scales. These include properties related to returns (leptokurtosis, absence of linear autocorrelation, volatility clustering), trading volumes (volume clustering, correlation between volume and volatility), and timing of trades (number of price changes, autocorrelation of durations between subsequent trades, heavy tails in their distribution, order-side clustering). Our model combines heterogeneous boundedly rational agents, endogenously activating on the basis of market events, with realistic assumptions on market microstructure. In particular, we introduce a strict event scheduling borrowed from the Euronext exchange. We study the model in a bottom-up fashion under alternative scenarios regarding the sophistication of agents' strategies. These scenarios allow us to disentangle the role of microstructure characteristics from trading behaviour in the emergence of market statistical properties. Our results reveal that traders' endogenous activation is crucial to jointly reproduce most of these properties. The ability of the model to replicate the main stylised facts of financial markets proves that it can be fruitfully used by policymakers as a test-bed for regulatory experiments aimed at improving market outcomes at different time-scales.

Suggested Citation

  • Jacopo Staccioli & Mauro Napoletano, 2021. "An agent-based model of intra-day financial markets dynamics," Sciences Po Economics Publications (main) halshs-03046657, HAL.
    • Handle: RePEc:hal:spmain:halshs-03046657
    DOI: 10.1016/j.jebo.2020.05.018
    Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-03046657v1
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    Cited by:

    1. Mateusz Wilinski & Anubha Goel & Alexandros Iosifidis & Juho Kanniainen, 2025. "Classifying and Clustering Trading Agents," Papers 2505.21662, arXiv.org.
    2. Dima, Bogdan & Dima, Ştefana Maria & Ioan, Roxana, 2025. "The short-run impact of investor expectations’ past volatility on current predictions: The case of VIX," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 98(C).
    3. Davide Bazzana & Michele Colturato & Roberto Savona, 2021. "Learning about Unprecedented Events: Agent-Based Modelling and the Stock Market Impact of COVID-19," Working Papers 2021.26, Fondazione Eni Enrico Mattei.
    4. Mateusz Wilinski & Juho Kanniainen, 2025. "Agent-based model of information diffusion in the limit order book trading," Papers 2508.20672, arXiv.org.
    5. Pastushkov, A., 2025. "Evolutionary and agent-based computational finance: The new paradigms for asset pricing," Journal of the New Economic Association, New Economic Association, vol. 66(1), pages 196-222.
    6. Nitika Sharma & Sridhar Manohar & Arjun J. Nair & A. B. Satish Rao, 2025. "Market microstructure to enhance sustainable investment decision and asset growth through financial literacy," Journal of Innovation and Entrepreneurship, Springer, vol. 14(1), pages 1-20, December.
    7. Bazzana, Davide & Colturato, Michele & Savona, Roberto, "undated". "Learning about Unprecedented Events: Agent-Based Modelling and the Stock Market Impact of COVID-19," FEEM Working Papers 314928, Fondazione Eni Enrico Mattei (FEEM).

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    Keywords

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    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • E12 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Keynes; Keynesian; Post-Keynesian; Modern Monetary Theory
    • E22 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Investment; Capital; Intangible Capital; Capacity
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • O4 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity

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