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Revisiting Cont's Stylized Facts for Modern Stock Markets

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Listed:
  • Ethan Ratliff-Crain
  • Colin M. Van Oort
  • James Bagrow
  • Matthew T. K. Koehler
  • Brian F. Tivnan

Abstract

In 2001, Rama Cont introduced a now-widely used set of 'stylized facts' to synthesize empirical studies of financial price changes (returns), resulting in 11 statistical properties common to a large set of assets and markets. These properties are viewed as constraints a model should be able to reproduce in order to accurately represent returns in a market. It has not been established whether the characteristics Cont noted in 2001 still hold for modern markets following significant regulatory shifts and technological advances. It is also not clear whether a given time series of financial returns for an asset will express all 11 stylized facts. We test both of these propositions by attempting to replicate each of Cont's 11 stylized facts for intraday returns of the individual stocks in the Dow 30, using the same authoritative data as that used by the U.S. regulator from October 2018 - March 2019. We find conclusive evidence for eight of Cont's original facts and no support for the remaining three. Our study represents the first test of Cont's 11 stylized facts against a consistent set of stocks, therefore providing insight into how these stylized facts should be viewed in the context of modern stock markets.

Suggested Citation

  • Ethan Ratliff-Crain & Colin M. Van Oort & James Bagrow & Matthew T. K. Koehler & Brian F. Tivnan, 2023. "Revisiting Cont's Stylized Facts for Modern Stock Markets," Papers 2311.07738, arXiv.org, revised May 2024.
    • Handle: RePEc:arx:papers:2311.07738
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    References listed on IDEAS

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    1. Todorova, Lora & Vogt, Bodo, 2011. "Power law distribution in high frequency financial data? An econometric analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(23), pages 4433-4444.
    2. Muller, Ulrich A. & Dacorogna, Michel M. & Dave, Rakhal D. & Olsen, Richard B. & Pictet, Olivier V. & von Weizsacker, Jacob E., 1997. "Volatilities of different time resolutions -- Analyzing the dynamics of market components," Journal of Empirical Finance, Elsevier, vol. 4(2-3), pages 213-239, June.
    3. Pagan, Adrian, 1996. "The econometrics of financial markets," Journal of Empirical Finance, Elsevier, vol. 3(1), pages 15-102, May.
    4. Silva, A. Christian & Yakovenko, Victor M., 2007. "Stochastic volatility of financial markets as the fluctuating rate of trading: An empirical study," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 382(1), pages 278-285.
    5. Moonis Shakeel & Bhavana Srivastava, 2021. "Stylized Facts of High-frequency Financial Time Series Data," Global Business Review, International Management Institute, vol. 22(2), pages 550-564, April.
    6. Muller, Ulrich A. & Dacorogna, Michel M. & Olsen, Richard B. & Pictet, Olivier V. & Schwarz, Matthias & Morgenegg, Claude, 1990. "Statistical study of foreign exchange rates, empirical evidence of a price change scaling law, and intraday analysis," Journal of Banking & Finance, Elsevier, vol. 14(6), pages 1189-1208, December.
    7. Y. Malevergne & V. Pisarenko & D. Sornette, 2005. "Empirical distributions of stock returns: between the stretched exponential and the power law?," Quantitative Finance, Taylor & Francis Journals, vol. 5(4), pages 379-401.
    8. Brian F Tivnan & David Rushing Dewhurst & Colin M Van Oort & John H Ring IV & Tyler J Gray & Brendan F Tivnan & Matthew T K Koehler & Matthew T McMahon & David M Slater & Jason G Veneman & Christopher, 2020. "Fragmentation and inefficiencies in US equity markets: Evidence from the Dow 30," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-24, January.
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