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Long Memory In Financial Time Series Data With Non-Gaussian Disturbances

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  • LUIS A. GIL-ALANA

    (Universidad de Navarra, Facultad de Ciencias Economicas, Edificio Biblioteca, Entrada Este, E-31080 Pamplona, Spain)

Abstract

In this article we propose the use of a version of the tests of Robinson [32] for testing unit and fractional roots in financial time series data. The tests have a standard null limit distribution and they are the most efficient ones in the context of Gaussian disturbances. We compute finite sample critical values based on non-Gaussian disturbances and the power properties of the tests are compared when using both, the asymptotic and the finite-sample (Gaussian and non-Gaussian) critical values. The tests are applied to the monthly structure of several stock market indexes and the results show that the if the underlyingI(0)disturbances are white noise, the confidence intervals include the unit root; however, if they are autocorrelated, the unit root is rejected in favour of smaller degrees of integration. Usingt-distributed critical values, the confidence intervals for the non-rejection values are generally narrower than with the asymptotic or than with the Gaussian finite-sample ones, suggesting that they may better describe the time series behaviour of the data examined.

Suggested Citation

  • Luis A. Gil-Alana, 2003. "Long Memory In Financial Time Series Data With Non-Gaussian Disturbances," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 6(02), pages 119-134.
    • Handle: RePEc:wsi:ijtafx:v:06:y:2003:i:02:n:s0219024903001827
    DOI: 10.1142/S0219024903001827
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    References listed on IDEAS

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    1. Paramsothy Silvapulle, 2001. "A Score Test For Seasonal Fractional Integration And Cointegration," Econometric Reviews, Taylor & Francis Journals, vol. 20(1), pages 85-104.
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