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Detecting the Fluctuations in Large Samples Using Wavelet Transform

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Listed:
  • S. AL Wadi
  • Ghassan Obeidat

Abstract

structure break is a famous features in stock market data that gain consideration from many kind of researchers. Generally, it occurs because of unexpected variations in the strategy of the government. Recently, wavelet method (WT) is more popular in the stock market data analysis since it has significant benefits than the other traditional methods. In this research paper, the discrete wavelet transform (DWT) based on Daubechies model will be used to capture the structure break in Amman stocks market /Jordan (ASE) using dataset from 2010 until 2018.

Suggested Citation

  • S. AL Wadi & Ghassan Obeidat, 2018. "Detecting the Fluctuations in Large Samples Using Wavelet Transform," Modern Applied Science, Canadian Center of Science and Education, vol. 12(12), pages 245-245, December.
    • Handle: RePEc:ibn:masjnl:v:12:y:2022:i:12:p:245
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    References listed on IDEAS

    as
    1. Yogo, Motohiro, 2008. "Measuring business cycles: A wavelet analysis of economic time series," Economics Letters, Elsevier, vol. 100(2), pages 208-212, August.
    2. Banerjee, Anindya & Urga, Giovanni, 2005. "Modelling structural breaks, long memory and stock market volatility: an overview," Journal of Econometrics, Elsevier, vol. 129(1-2), pages 1-34.
    3. Gençay, Ramazan & Gençay, Ramazan & Selçuk, Faruk & Whitcher, Brandon J., 2001. "An Introduction to Wavelets and Other Filtering Methods in Finance and Economics," Elsevier Monographs, Elsevier, edition 1, number 9780122796708.
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    More about this item

    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

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