Abstract
We introduce a new method for detection of long-range cross- correlations and cross-multifractality { multifractal height cross-correlation analysis (MF-HXA). MF-HXA is a multivariate generalization of the height-height correlation analysis.
We show that long-range cross-correlations can be caused by a mixture of the following { long-range dependence of separate processes and additional scaling of covariances between the processes. Similar separation applies for cross-multifractality - standard separation between distributional properties and correlations is enriched by division of correlations between auto-correlations and cross-correlations.
We further apply the method on returns and volatility of NASDAQ and S&P500 indices as well as of Crude and Heating Oil futures and uncover some interesting results.