Quantifying And Modeling Long-range Cross-correlations In Multiple Time Series With Applications To World Stock Indices
The authors propose a modified time lag random matrix theory in order to study time lag cross-correlations in multiple time series. They apply the method to 48 world indices, one for each of 48 different countries. They find long-range power-law cross-correlations in the absolute values of returns that quantify risk, and find that they decay much more slowly than cross-correlations between the returns. The magnitude of the cross-correlations constitute "Bad news" for international investment managers who may believe that risk is reduced by diversifying across countries.