The noise component is due to the unavailability of complete information from the signal behaviour to capture the dependency between past and future values. The financial time series are inherently noisy, nonstationary, and deterministic chaotic, that is to say the distribution of financial time series is changing over the time.
The understanding of such relations would be helpful to the design of good portfolios. Statistical motivations to visualize correlations in order to suggest some potentially plausible parameter relations and restrictions. Economic motivations to identify the main factors which affect the behavior of stock markets across different exchanges and countries. The study of the international stock markets may have different leitmotifs. This paper proposes a method for analyzing the correlations embedded in international stock markets. IntroductionĮconomical indices measure segments of the stock market and are normally used to benchmark the performance of stock portfolios. Wavelets are also tested, and the corresponding MDS plots are analyzed. Of the SMI dynamics, while their comparison is visualized by means of the MDS. In a first phase, the real Shannon wavelet is adopted for performing the evaluation Information content of the stock signals. Six continuous wavelets are tested for analyzing the Wavelet transform and the multidimensional scaling (MDS). This paper studies the dynamics of SMI by combining the Limitations in discriminating different periods of time.
Classical methods, such as the Fourier transform, reveal considerable Tools for analyzing their characteristics. Considerable researchĮfforts during the last years demonstrated that these signals have a chaotic nature and require sophisticated mathematical Stock market indices (SMIs) are important measures of financial and economical performance.
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