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Modeling long-term memory effect in stock prices: A comparative analysis with GPH test and Daubechies wavelets

Author

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  • Alper Ozun
  • Atilla Cifter

Abstract

Purpose - This paper, using Turkish stock index data, set outs to present long-term memory effect using chaotic and conventional unit root tests and investigate if chaotic technique as wavelets captures long-memory better than conventional techniques. Design/methodology/approach - Haar and Daubechies as wavelet-based OLS estimator and GPH and other classical models are applied in order to investigate the performance of long memory in the time series. Findings - The results indicate that Daubechies wavelet analysis provide the accurate determination for long memory where conventional techniques does not. Originality/value - The research results have both methodological and practical originality. On the theoretical side, the wavelet-based OLS estimator is superior in modeling the behaviours of the stock returns in emerging markets where non-linearities and high volatility exist due to their chaotic natures. For practical aims, on the other hand, the results show that the Istanbul Stock Exchange is not in the weak-form efficient because the prices have memories that are not reflected in the prices, yet.

Suggested Citation

  • Alper Ozun & Atilla Cifter, 2008. "Modeling long-term memory effect in stock prices: A comparative analysis with GPH test and Daubechies wavelets," Studies in Economics and Finance, Emerald Group Publishing, vol. 25(1), pages 38-48, March.
  • Handle: RePEc:eme:sefpps:v:25:y:2008:i:1:p:38-48
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    References listed on IDEAS

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    1. Christoph Schleicher, 2002. "An Introduction to Wavelets for Economists," Staff Working Papers 02-3, Bank of Canada.
    2. repec:wsi:ijtafx:v:07:y:2004:i:05:n:s021902490400258x is not listed on IDEAS
    3. Tkacz Greg, 2001. "Estimating the Fractional Order of Integration of Interest Rates Using a Wavelet OLS Estimator," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 5(1), pages 1-15, April.
    4. Nason, G.P. & von Sachs, R., 1999. "Wavelets in Time Series Analysis," Papers 9901, Catholique de Louvain - Institut de statistique.
    5. Erhan Bayraktar & H. Vincent Poor & K. Ronnie Sircar, 2004. "Estimating The Fractal Dimension Of The S&P 500 Index Using Wavelet Analysis," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 7(05), pages 615-643.
    6. Crowley, Patrick M., 2005. "An intuitive guide to wavelets for economists," Research Discussion Papers 1/2005, Bank of Finland.
    7. Barkoulas, John T. & Baum, Christopher F., 1996. "Long-term dependence in stock returns," Economics Letters, Elsevier, vol. 53(3), pages 253-259, December.
    8. Jensen, Mark J., 2000. "An alternative maximum likelihood estimator of long-memory processes using compactly supported wavelets," Journal of Economic Dynamics and Control, Elsevier, vol. 24(3), pages 361-387, March.
    9. Sowell, Fallaw, 1990. "The Fractional Unit Root Distribution," Econometrica, Econometric Society, vol. 58(2), pages 495-505, March.
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    Cited by:

    1. Quinton Morris & Gary Van vuuren & Paul Styger, 2009. "Further Evidence Of Long Memory In The South African Stock Market," South African Journal of Economics, Economic Society of South Africa, vol. 77(1), pages 81-101, March.
    2. Yalama, Abdullah & Celik, Sibel, 2013. "Real or spurious long memory characteristics of volatility: Empirical evidence from an emerging market," Economic Modelling, Elsevier, vol. 30(C), pages 67-72.

    More about this item

    Keywords

    Economic cycles; Stock prices; Emerging markets; Turkey;

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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