Department of Pattern Recognition

(eng): We introduce a new method for detection of long-range cross-correlations and cross-multifractality – multifractal height cross-correlation analysis (MF-HXA). We show that long-range cross-correlations can be caused by long-range dependence of separate processes and the correlations above them. Similar separation applies for cross-multifractality – standard sep- aration between distributional properties and correlations is enriched by division of correlations between auto-correlations and cross-correlations. Efficiency of the method is showed on two types of simulated series – ARFIMA and Mandelbrot’s Binomial Multifractal model. We further ap- ply the method on returns and volatility of NASDAQ and S&P500 indices and uncover some interesting results.