  The aim here is to study the concept of pairing multifractality between time
series possessing non-Gaussian distributions. The increasing number of rare
events creates "criticality". We show how the pairing between two series is
affected by rare events, which we call "coupled criticality". A method is
proposed for studying the coupled criticality born out of the interaction
between two series, using the bivariate multifractal random walk (BiMRW). This
method allows studying dependence of the coupled criticality on the criticality
of each individual system. This approach is applied to data sets of gold and
oil markets, and inflation and unemployment.
