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Rock around the clock: An agent-based model of low- and high-frequency trading

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  • Sandrine Jacob Leal

    ()

  • Mauro Napoletano

    ()

  • Andrea Roventini

    ()

  • Giorgio Fagiolo

    ()

Abstract

We build an agent-based model to study how the interplay between low- and high-frequency trading affects asset price dynamics. Our main goal is to investigate whether high-frequency trading exacerbates market volatility and generates flash crashes. In the model, low-frequency agents adopt trading rules based on chronological time and can switch between fundamentalist and chartist strategies. By contrast, high-frequency traders activation is event-driven and depends on price fluctuations. High-frequency traders use directional strategies to exploit market information produced by low-frequency traders. Monte-Carlo simulations reveal that the model replicates the main stylized facts of financial markets. Furthermore, we find that the presence of high-frequency traders increases market volatility and plays a fundamental role in the generation of flash crashes. The emergence of flash crashes is explained by two salient characteristics of high-frequency traders, i.e., their ability to i. generate high bid-ask spreads and ii. synchronize on the sell side of the limit order book. Finally, we find that higher rates of order cancellation by high-frequency traders increase the incidence of flash crashes but reduce their duration. Copyright Springer-Verlag Berlin Heidelberg 2016

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  • Sandrine Jacob Leal & Mauro Napoletano & Andrea Roventini & Giorgio Fagiolo, 2016. "Rock around the clock: An agent-based model of low- and high-frequency trading," Journal of Evolutionary Economics, Springer, vol. 26(1), pages 49-76, March.
  • Handle: RePEc:spr:joevec:v:26:y:2016:i:1:p:49-76
    DOI: 10.1007/s00191-015-0418-4
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    Cited by:

    1. Zakaria Babutsidze & Maurizio Iacopetta, 2016. "Innovation, growth and financial markets," Journal of Evolutionary Economics, Springer, vol. 26(1), pages 1-24, March.
    2. repec:eee:phsmap:v:486:y:2017:i:c:p:618-627 is not listed on IDEAS
    3. Francesco Lamperti & Andrea Roventini & Amir Sani, 2017. "Agent-Based Model Calibration using Machine Learning Surrogates," Sciences Po publications 2017-09, Sciences Po.
    4. Nathalie Oriol & Iryna Veryzhenko, 2015. "Market structure or traders’ behavior? An assessment of flash crash phenomena and their regulation based on a multi-agent simulation," Working Papers halshs-01254435, HAL.
    5. Sandrine Jacob Leal & Mauro Napoletano, 2016. "Market Stability vs. Market Resilience: Regulatory Policies Experiments in an Agent-Based Model with Low- and High- Frequency Trading," Sciences Po publications 2016-12, Sciences Po.
    6. repec:spr:annopr:v:260:y:2018:i:1:d:10.1007_s10479-016-2286-1 is not listed on IDEAS
    7. repec:eee:jfinec:v:128:y:2018:i:2:p:253-265 is not listed on IDEAS
    8. 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.
    9. 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.
    10. James Paulin & Anisoara Calinescu & Michael Wooldridge, 2018. "Understanding Flash Crash Contagion and Systemic Risk: A Micro-Macro Agent-Based Approach," Papers 1805.08454, arXiv.org.
    11. Erhan Bayraktar & Alexander Munk, 2017. "Mini-Flash Crashes, Model Risk, and Optimal Execution," Papers 1705.09827, arXiv.org.

    More about this item

    Keywords

    Agent-based models; Limit order book; High-frequency trading; Low-frequency trading; Flash crashes; Market volatility; G12; G01; G14; C63;

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G01 - Financial Economics - - General - - - Financial Crises
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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