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Rock around the Clock: An Agent-Based Model of Low- and High-Frequency Trading

Author Info

Listed author(s):
  • 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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Paper provided by Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy in its series LEM Papers Series with number 2014/03.

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Date of creation: 02 Apr 2014
Handle: RePEc:ssa:lemwps:2014/03
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Keywords: Agent-based models; Limit order book; High-frequency trading; Low-frequency trading; Flash crashes; Market volatility;

Other versions of this item:

Find related papers by 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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Cited by:

  1. Francesco Lamperti & Andrea Roventini & Amir Sani, 2017. "Agent-Based Model Calibration using Machine Learning Surrogates," Sciences Po publications 2017-09, Sciences Po.
  2. 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.
  3. Zakaria Babutsidze & Maurizio Iacopetta, 2016. "Innovation, growth and financial markets," Journal of Evolutionary Economics, Springer, vol. 26(1), pages 1-24, March.
  4. 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," Documents de Travail de l'OFCE 2016-12, Observatoire Francais des Conjonctures Economiques (OFCE).
  5. Erhan Bayraktar & Alexander Munk, 2017. "Mini-Flash Crashes, Model Risk, and Optimal Execution," Papers 1705.09827, arXiv.org.

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