DocumentCode
2654436
Title
Notice of Retraction
Detecting the regime shift via wavelet transform
Author
Al Wadi, S. ; Ismail, M.T.
Author_Institution
Sch. of Math. Sci., Univ. Sains Malaysia, Minden, Malaysia
Volume
7
fYear
2010
fDate
16-18 April 2010
Abstract
Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
Recently, regime shifts or structure breaks had acquired very high attention in analyzing financial time series data. Abrupt of changes in the government policy, financial crises and many of challenges lead to change in the behavior of the financial time series data. In addition, wavelet transform also becomes very famous in the financial sector and it has better advantages than the other filtering methods such as the traditional technique Fourier transform. Therefore, to prove the high ability for the wavelet transform, Haar wavelet transform (HWT) and fast Fourier transform are used to capture the possibility of regime shifts or structure breaks. Apart from detecting precisely the change, this paper also discuss advantages and disadvantages for each method using Amman stocks market (Jordan) between 1992 and 2008. Some numerical and statistical results will be presented in this paper.
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
Recently, regime shifts or structure breaks had acquired very high attention in analyzing financial time series data. Abrupt of changes in the government policy, financial crises and many of challenges lead to change in the behavior of the financial time series data. In addition, wavelet transform also becomes very famous in the financial sector and it has better advantages than the other filtering methods such as the traditional technique Fourier transform. Therefore, to prove the high ability for the wavelet transform, Haar wavelet transform (HWT) and fast Fourier transform are used to capture the possibility of regime shifts or structure breaks. Apart from detecting precisely the change, this paper also discuss advantages and disadvantages for each method using Amman stocks market (Jordan) between 1992 and 2008. Some numerical and statistical results will be presented in this paper.
Keywords
Haar transforms; financial management; stock markets; time series; wavelet transforms; Amman stocks market; Haar wavelet transform; fast Fourier transform; financial crises; financial time series data analysis; government policy; regime shift detection; structure breaks; Discrete Fourier transforms; Discrete wavelet transforms; Fast Fourier transforms; Fourier transforms; Multiresolution analysis; Stock markets; Time series analysis; Wavelet analysis; Wavelet domain; Wavelet transforms; Amman stocks market; Fourier transform; Structure break; Wavelet transform;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Engineering and Technology (ICCET), 2010 2nd International Conference on
Conference_Location
Chengdu
Print_ISBN
978-1-4244-6347-3
Type
conf
DOI
10.1109/ICCET.2010.5485691
Filename
5485691
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