Dynamics of cross-correlations in the stock market

Co-movements of stock price fluctuations are described by the cross-correlation matrix . The application of random matrix theory (RMT) allows to distinguish between spurious correlations in due to measurement noise and true correlations containing economically meaningful information. By calculating cross-correlations for different time windows, we study the time dependence of eigenvectors of , which are related to economic sectors, and the time evolution of the largest eigenvalue, which describes the average correlation strength. We use these results to forecast cross-correlations, and test the quality of our forecast by constructing investments in the stock market which expose the invested capital to a minimum level of risk only.