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Segmentation Based Testing of Co-Movement Significance

The paper deals with the significance testing of the time-frequency co-movement measure on the segmentation bases. When investigating the test of the power wavelet cross-spectrum we have consider standard assumptions, i.e. two independent Gaussian white noise inputs. Then, with the use of the Bessel function, we can test whether the values of power wavelet cross-spectrum are significant with respect to the variance of each input time series. The paper investigates the case when input data have a heteroscedastic character. Thus, we first propose a segmentation of the data sample according to the variances of input time series. Then, we propose an identification of significant power wavelet cross-spectrum values in each segment via Ge test. The results with and without the segmentation are compared. The experiment is performed on simulated and real data. The results show that the segmentation based testing for the heteroscedastic data provides more precise results.