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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 chrono- logical time and can switch between fundamentalist and chartist strategies. On the contrary, high-frequency traders activation is event-driven and depends on price fluctuations. High-frequency traders use directional strategies to exploit market in- formation 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 trading 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.

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  • Sandrine Jacob Leal & Mauro Napoletano & Andrea Roventini & Giorgio Fagiolo, 2014. "Rock around the Clock: An Agent-Based Model of Low- and High-Frequency Trading," LEM Papers Series 2014/03, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    • Handle: RePEc:ssa:lemwps:2014/03
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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. 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.
    3. repec:kap:fmktpm:v:33:y:2019:i:2:d:10.1007_s11408-019-00331-6 is not listed on IDEAS
    4. repec:eee:phsmap:v:486:y:2017:i:c:p:618-627 is not listed on IDEAS
    5. 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.
    6. 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.
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    12. Birte Ewers & Jonathan F. Donges & Jobst Heitzig & Sonja Peterson, 2019. "Divestment may burst the carbon bubble if investors' beliefs tip to anticipating strong future climate policy," Papers 1902.07481, arXiv.org.
    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.
    14. 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.
    15. repec:eee:dyncon:v:100:y:2019:i:c:p:200-229 is not listed on IDEAS
    16. 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.
    17. Blaurock, Ivonne & Schmitt, Noemi & Westerhoff, Frank, 2018. "Market entry waves and volatility outbursts in stock markets," Journal of Economic Behavior & Organization, Elsevier, vol. 153(C), pages 19-37.
    18. Erhan Bayraktar & Alexander Munk, 2017. "Mini-Flash Crashes, Model Risk, and Optimal Execution," Papers 1705.09827, arXiv.org, revised Aug 2018.

    More about this item

    Keywords

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

    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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