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Long memory and Periodicity in Intraday Volatility

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Author Info

  • Eduardo Rossi

    (Department of Economics and Management, University of Pavia)

  • Dean Fantazzini

    (Moscow School of Economics, M.V. Lomonosov Moscow State University)

Abstract

Intraday return volatilities are characterized by the contemporaneous presence of periodicity and long memory. This paper proposes two new parameterizations of the intraday volatility: the Fractionally Integrated Periodic EGARCH and the Seasonal Fractional Integrated Periodic EGARCH, which provide the required flexibility to account for both features. The periodic kurtosis and periodic autocorrelations of power transformations of the absolute returns are computed for both models. The empirical application shows that volatility of the hourly Emini S&P 500 futures returns are characterized by a periodic leverage effect coupled with a statistically significant long-range dependence. An out-of-sample forecasting comparison with alternative models shows that a constrained version of the FI-PEGARCH provides superior forecasts. A simulation experiment is carried out to investigate the effects that sample frequency has on the fractional differencing parameter estimate.

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Bibliographic Info

Paper provided by University of Pavia, Department of Economics and Management in its series DEM Working Papers Series with number 015.

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Length: 41 pages
Date of creation: Nov 2012
Date of revision:
Handle: RePEc:pav:demwpp:015

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Related research

Keywords: Intraday volatility; Long memory; FI-PEGARCH; SFI-PEGARCH; Periodicmodels.;

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References

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  1. Beltratti, Andrea & Morana, Claudio, 1999. "Computing value at risk with high frequency data," Journal of Empirical Finance, Elsevier, vol. 6(5), pages 431-455, December.
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  12. Philip Hans Franses & Richard Paap, 2000. "Modelling day-of-the-week seasonality in the S&P 500 index," Applied Financial Economics, Taylor & Francis Journals, vol. 10(5), pages 483-488.
  13. Ruiz, Esther & Veiga, Helena, 2008. "Modelling long-memory volatilities with leverage effect: A-LMSV versus FIEGARCH," Computational Statistics & Data Analysis, Elsevier, vol. 52(6), pages 2846-2862, February.
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  15. Baillie, Richard T. & Bollerslev, Tim & Mikkelsen, Hans Ole, 1996. "Fractionally integrated generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 74(1), pages 3-30, September.
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Cited by:
  1. Barbara Bedowska-Sojka, 2011. "The Impact of Macro News on Volatility of Stock Exchanges," Dynamic Econometric Models, Uniwersytet Mikolaja Kopernika, vol. 11, pages 99-110.

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