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Long Memory and Data Frequency in Financial Markets

Author

Listed:
  • Guglielmo Maria Caporale
  • Luis A. Gil-Alana
  • Alex Plastun
Abstract
This paper investigates persistence in financial time series at three different frequencies (daily, weekly and monthly). The analysis is carried out for various financial markets (stock markets, FOREX, commodity markets) over the period from 2000 to 2016 using two different long memory approaches (R/S analysis and fractional integration) for robustness purposes. The results indicate that persistence is higher at lower frequencies, for both returns and their volatility. This is true of the stock markets (both developed and emerging) and partially of the FOREX and commodity markets examined. Such evidence against the random walk behavior implies predictability and is inconsistent with the Efficient Market Hypothesis (EMH), since abnormal profits can be made using specific option trading strategies (butterfly, straddle, strangle, iron condor, etc.).

Suggested Citation

  • Guglielmo Maria Caporale & Luis A. Gil-Alana & Alex Plastun, 2017. "Long Memory and Data Frequency in Financial Markets," CESifo Working Paper Series 6396, CESifo.
    • Handle: RePEc:ces:ceswps:_6396
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    References listed on IDEAS

    as
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    Cited by:

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    2. Long Hai Vo & Duc Hong Vo, 2020. "Modelling Australian Dollar Volatility at Multiple Horizons with High-Frequency Data," Risks, MDPI, vol. 8(3), pages 1-16, August.
    3. Naveen Musunuru, 2019. "Modeling Long Range Dependence in Wheat Food Price Returns," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 11(9), pages 1-46, September.
    4. Antoniades, I.P. & Brandi, Giuseppe & Magafas, L. & Di Matteo, T., 2021. "The use of scaling properties to detect relevant changes in financial time series: A new visual warning tool," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).
    5. Omane-Adjepong, Maurice & Boako, Gidoen & Alagidede, Paul, 2018. "Modelling heterogeneous speculation in Ghana’s foreign exchange market: Evidence from ARFIMA-FIGARCH and Semi-Parametric methods," MPRA Paper 86617, University Library of Munich, Germany.

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    More about this item

    Keywords

    persistence; long memory; R; S analysis; fractional integration;
    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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