Before revision:  Firstly, we used the most common linear analysis, based on cointegrationand Granger causality tests ; secondly we applied a nonlinear approach, using mutual information to analyze nonlinear dependence. Since underlying data sets are affected by non-stationarities  , we also applied MF-DFA and MF-DXA in order to examine the multifractality nature of data and to analyze the relationship and mutual interaction between pairs of series, respectively.
After revision:  Firstly, we used the most common linear analysis, based on cointegrationand Granger causality tests and nonlinear approaches in context of mutual information and correlations. Underlying data sets are affected by non-stationarities , we also applied MF-DFA and MF-DXA in order to examine the multifractality nature of data and to analyze the relationship and mutual interaction between pairs of series, respectively.