 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.
<clarity>  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.
 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.