Abstract. In this work the core objective is to apply discrete wavelet transform (DWT) functions namely Haar, Daubechies, Symmlet, Coiflet and discrete approximation of the meyer wavelets in non-stationary financial time series data from US stock market (DJIA30). The data consists of 2048 daily data of closing index starting from December 17, 2004 until October 23, 2012. From the unit root test the results show that the data is non stationary in the level. In order to study the stationarity of a time series, the autocorrelation function (ACF) is used. Results indicate that, Haar function is the lowest function to obtain noisy series as compared to Daubechies, Symmlet, Coiflet and discrete approximation of the meyer wavelets. In addition, the original data after decomposition by DWT is less noisy series than decomposition by DWT for return time series.

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This paper is based on an investigation to explore the dynamic relationship between US stock market index and three other stock indices of the Middle East and North Africa (MENA). This is accomplished by using discrete wavelet filtering, applied to daily data set from 6/29/2001 to 5/5/2009. After that cointegration test and VEC model are used to determine the long run and short run relationship between these stock markets. Cointegration test confirms the existence of cointegration between the studied series, and shows that there is a long run relationship between the US stock market and MENA stock markets, while the VEC model shows that there is a short run relationship between the aforesaid stock markets.