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Volatility Computational in Financial Time Series Data Based on Wavelet Transforms

S. Al Wadi, Mohd Tahir Ismail, Samsul Ariffin Addul Karim


During the financial crises that occurred everywhere for many reasons in the whole world and through the developments in the financial and economic sectors. So in this paper, we will focus on the most important concept in risk management and financial time series, namely volatility. It is well known that wavelet transforms are one of the most famous spectral filtering methods that have recently developed very quickly. Consequently, we compare these methods with the traditional technique, i.e., Fast Fourier transform, to decide the best method for decomposing volatility. The comparison was done under some of the statistical rules. Matlab software, some statistical packages and Amman stock markets (Jordan) were selected in this study (1992-2008).


Haar wavelet transform, Daubechies wavelet transform, Fast Fourier transform, volatility, stocks market, Matlab (Wavelet tools).

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