Detecting the Regime Shift via Wavelet Transform

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. KeywordsWavelet transform, Fourier transform, Structure break, Amman stocks market.