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Modeling long‐term memory effect in stock prices

Author

Listed:
  • 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," Studies in Economics and Finance, Emerald Group Publishing Limited, vol. 25(1), pages 38-48, March.
    • Handle: RePEc:eme:sefpps:v:25:y:2008:i:1:p:38-48
    DOI: 10.1108/10867370810857559
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    Citations

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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. Anju Bala & Kapil Gupta, 2020. "Examining The Long Memory In Stock Returns And Liquidity In India," Copernican Journal of Finance & Accounting, Uniwersytet Mikolaja Kopernika, vol. 9(3), pages 25-43.
    3. 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.
    4. Kunal Saha & Vinodh Madhavan & Chandrashekhar G. R. & David McMillan, 2020. "Pitfalls in long memory research," Cogent Economics & Finance, Taylor & Francis Journals, vol. 8(1), pages 1733280-173, January.

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