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Long-Run Volatility Dependencies in Intraday Data and Mixture of Normal Distributions

Author

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  • Aurélie Boubel

    (University of Evry, EPEE)

  • Sébastien Laurent

    (University of Liège)

Abstract

In this paper, we study the behaviour of the long memory in the return volatility using highfrequency data on the Deutschemark-US dollar. In particular, we provide evidence of the overestimation of the long memory when we do not take into account the presence of jumps (outliers) in the series. After filtering the series from its seasonal pattern, and by using a mixture of normal distributions, the long memory parameter is found to be constant across different sampling frequencies, reduced (compared to the normal distribution) but still significant.

Suggested Citation

  • Aurélie Boubel & Sébastien Laurent, 2000. "Long-Run Volatility Dependencies in Intraday Data and Mixture of Normal Distributions," Documents de recherche 00-13, Centre d'Études des Politiques Économiques (EPEE), Université d'Evry Val d'Essonne.
    • Handle: RePEc:eve:wpaper:00-13
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    References listed on IDEAS

    as
    1. Neely, Christopher J., 1999. "Target zones and conditional volatility: The role of realignments," Journal of Empirical Finance, Elsevier, vol. 6(2), pages 177-192, April.
    2. Lamoureux, Christopher G & Lastrapes, William D, 1990. "Persistence in Variance, Structural Change, and the GARCH Model," Journal of Business & Economic Statistics, American Statistical Association, vol. 8(2), pages 225-234, April.
    Full references (including those not matched with items on IDEAS)

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