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Long Memory and Fractional Integration in High Frequency Financial Time Series

Author

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  • Guglielmo Maria Caporale
  • Luis A. Gil-Alana

Abstract

This paper analyses the long-memory properties of high frequency financial time series. It focuses on temporal aggregation and the influence that this might have on the degree of dependence of the series. Fractional integration or I(d) models are estimated with a variety of specifications for the error term. In brief, we find evidence that a lower degree of integration is associated with lower data frequencies. In particular, when the data are collected every 10 minutes there are several cases with values of d strictly smaller than 1, implying mean-reverting behaviour. This holds for all four series examined, namely Open, High, Low and Last observations for the British pound/US dollar spot exchange rate.

Suggested Citation

  • Guglielmo Maria Caporale & Luis A. Gil-Alana, 2010. "Long Memory and Fractional Integration in High Frequency Financial Time Series," Discussion Papers of DIW Berlin 1016, DIW Berlin, German Institute for Economic Research.
    • Handle: RePEc:diw:diwwpp:dp1016
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    File URL: https://www.diw.de/documents/publikationen/73/diw_01.c.357365.de/dp1016.pdf
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    Cited by:

    1. Dunis, Christian & Kellard, Neil M. & Snaith, Stuart, 2013. "Forecasting EUR–USD implied volatility: The case of intraday data," Journal of Banking & Finance, Elsevier, vol. 37(12), pages 4943-4957.

    More about this item

    Keywords

    High frequency data; long memory; volatility persistence; structural breaks;
    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

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