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|Title:||Modeling long-term memory effect in stock prices: A comparative analysis with GPH test and Daubechies wavelets|
|Author(s):||Alper Ozun, (IsBank of Turkey, Istanbul, Turkey), Atilla Cifter, (Department of Econometrics, Sekerbank and Marmara University, Istanbul, Turkey)|
|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, Vol. 25 Iss: 1, pp.38 - 48|
|Keywords:||Economic cycles, Emerging markets, Stock prices, Turkey|
|Article type:||Research paper|
|DOI:||10.1108/10867370810857559 (Permanent URL)|
|Publisher:||Emerald Group Publishing Limited|
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.
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