Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

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

Modeling long‐term memory effect in stock prices A comparative analysis with GPH test and... 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Studies in Economics and Finance Emerald Publishing

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

Alper Ozun; Atilla Cifter
Studies in Economics and Finance , Volume 25 (1): 11 – Mar 7, 2008
Download PDF
11 pages

Loading next page...
 
/lp/emerald-publishing/modeling-long-term-memory-effect-in-stock-prices-a-comparative-0cQ6WtBs3w
Publisher
Emerald Publishing
Copyright
Copyright © 2008 Emerald Group Publishing Limited. All rights reserved.
ISSN
1086-7376
DOI
10.1108/10867370810857559
Publisher site
See Article on Publisher Site

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.

Journal

Studies in Economics and Finance – Emerald Publishing

Published: Mar 7, 2008

Keywords: Economic cycles; Stock prices; Emerging markets; Turkey

References

  • Long‐term dependence in stock returns
    Barkoulas, J.T.; Baum, C.F.
  • Estimating the fractal dimension of the S&P 500 index using wavelet analysis
    Bayraktar, E.; Poor, H.V.; Sircar, K.R.
  • An intuitive guide to wavelets for economists
    Crowley, P.
  • Ortonormal bases of compactly supported wavelets
    Daubechies, I.
  • Compression of wavelet decompositions
    Devore, R.; Jawerth, B.; Popov, V.
  • Likelihood ratio statistics for an autoregressive time series with a unit root
    Dickey, D.A.; Fuller, W.A.