WAVELET ESTIMATORS FOR LONG MEMORY IN STOCK MARKETS

Anouar Ben Mabrouk (), Hedi Kortas () and Samir Ben Ammou ()
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Anouar Ben Mabrouk: Computational Mathematics Laboratory, Department of Mathematics, Faculty of Sciences, 5000 Monastir, Tunisia
Hedi Kortas: Higher Institute of Management, Department of Quantitative, Methods, Street of Abdel-Aziz El-Bahi, 4000 Sousse, Tunisia
Samir Ben Ammou: Computational Mathematics Laboratory, Department of Mathematics, Faculty of Sciences, 5000 Monastir, Tunisia

International Journal of Theoretical and Applied Finance (IJTAF), 2009, vol. 12, issue 03, 297-317

Abstract: In this paper, fractional integrating dynamics in the return and the volatility series of stock market indices are investigated. The investigation is conducted using wavelet ordinary least squares, wavelet weighted least squares and the approximate Maximum Likelihood estimator. It is shown that the long memory property in stock returns is approximately associated with emerging markets rather than developed ones while strong evidence of long range dependence is found for all volatility series. The relevance of the wavelet-based estimators, especially, the approximate Maximum Likelihood and the weighted least squares techniques is proved in terms of stability and estimation accuracy.

Keywords: Wavelets; long memory; stock market; volatility (search for similar items in EconPapers)
Date: 2009
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Citations: View citations in EconPapers (3)

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DOI: 10.1142/S0219024909005233

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