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

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.