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Persistence in High Frequency Financial Data

Author

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  • Guglielmo Maria Caporale
  • Alex Plastun

Abstract

This paper investigates persistence in high-frequency, intraday data (and also daily and monthly ones) in the case of the EuroStoxx 50 futures over the period from 2002 to 2018 (720 million trade records) using R/S analysis and the Hurst exponent as a measure of persistence. The results indicate that persistence is sensitive to the data frequency. More specifically, monthly data are highly persistent, daily ones follow a random walk, and intraday ones are anti-persistent. In addition, persistence varies over time. These findings imply that the Efficient Market Hypothesis (EMH) only holds in the case of daily data, whilst it is possible to make abnormal profits using trading strategies based on reversal strategies at the intraday frequency.

Suggested Citation

  • Guglielmo Maria Caporale & Alex Plastun, 2022. "Persistence in High Frequency Financial Data," CESifo Working Paper Series 10045, CESifo.
    • Handle: RePEc:ces:ceswps:_10045
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    File URL: https://www.cesifo.org/DocDL/cesifo1_wp10045.pdf
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    References listed on IDEAS

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    More about this item

    Keywords

    persistence; long memory; R/S analysis; high-frequency data;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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