Wavelet Correlation Coefficient of 'strongly correlated' financial time series

In this paper we use wavelet concepts to show that correlation coefficient between two financial data's is not constant but varies with scale from high correlation value to strongly anti-correlation value This studies is important because correlation coefficient is used to quantify degree of independence between two variables. In econophysics correlation coefficient forms important input to evolve hierarchial tree and minimum spanning tree of financial data.Comment: physica A (in press

計量経済学における相関有無の判定基準への疑問

In econometrics it is important to distinguish whether two variables are correlated or not. Distribution of correlation coefficient of n pairs of samples from two independent normal populations is theoretically known. Based on the distribution and the value of the sample correlation coefficient we decide whether the two variables are correlated or not. We compute distribution of correlation coefficient of n pairs of samples from two independent uniform populations by computer simulation. We see that we cannot distinguish the two distributions of the correlation coefficients when n11, however, the two distributions are different definitely