Towards identifying the world stock market cross-correlations: DAX versus Dow Jones

Abstract

Effects connected with the world globalization affect also the financial markets. On a way towards quantifying the related characteristics we study the financial empirical correlation matrix of the 60 companies which both the Deutsche Aktienindex (DAX) and the Dow Jones (DJ) industrial average comprised during the years 1990-1999. The time-dependence of the underlying cross-correlations is monitored using a time window of 60 trading days. Our study shows that if the time-zone delays are properly accounted for the two distant markets largely merge into one. This effect is particularly visible during the last few years. It is however the Dow Jones which dictates the trend.

Correlation structure and dynamics of international real estate securities markets: A network perspective

“…1 and find that the distribution of the correlation coefficients clearly deviates from a Gaussian-like shape and shows a bi-modal form, which implies that the distribution originates from something like two, to some degree disconnected, markets going their own, perhaps somewhat independent dynamics and similar to a situation discussed by Drożdż et al [54,55]. From Fig.…”

“…Combining these two findings raised above, one can infer that international real estate securities markets may be divided into two invisible (or virtual) markets, i.e., the Asian-Pacific market and the European market, which are two independent markets. To confirm this inference, following Drożdż et al [54], we consider that the global correlation matrix C has the following block structure:…”

“…1 in Ref. [54], which may be attributed to different algorithms that are used to generate the PDF figures. In this paper, we employ the Kernel smoothing density estimate algorithm [56] to plot the PDF.…”

Correlation of financial markets in times of crisis

“…This approach was successfully applied to a large number of financial markets [71]- [100], and also to the relation between world markets [101]- [102]. This approach was also used in the construction of hierarchical structures between different assets of financial markets [103]- [142].…”

Multifractality in the random parameter model for multivariate time series

“…Also, a number of results have been observed that are not in perfect agreement with RMT, such as the observation that noise eigenvalues seem to be a little larger than expected [8] and that correlations can be measured in the supposedly random part of the eigenvalue spectrum [9,10]. It has also been verified different behaviors of the eigenvalues corresponding to different points of time, suggesting that non-stationary effects might play an important role [11,12]. The Random Parameter model, recently proposed by one of the authors [13,14], tries to fit the complete structure of the correlation matrix, based on parameters that can be interpreted as typical observations of the system.…”