Stock market comovements are examined using cointegration, Granger causality
tests and nonlinear approaches in context of mutual information and
correlations. Underlying data sets are affected by non-stationarities and
trends, we also apply AMF-DFA and AMF-DXA. We find only 170 pair of Stock
markets cointegrated, and according to the Granger causality and mutual
information, we realize that the strongest relations lies between emerging
markets, and between emerging and frontier markets. According to scaling
exponent given by AMF-DFA, h(q=2)>1, we find that all underlying data sets
belong to non-stationary process. According to EMH, only 8 markets are
classified in uncorrelated processes at 2σ confidence interval. 6 Stock
markets belong to anti-correlated class and dominant part of markets has memory
in corresponding daily index prices during January 1995 to February 2014.
New-Zealand with H=0.457±0.004 and Jordan with H=0.602±0.006 are far
from EMH. The nature of cross-correlation exponents based on AMF-DXA is almost
multifractal for all pair of Stock markets. The empirical relation, Hxy≤[Hxx+Hyy]/2, is confirmed. Mentioned relation for q>0 is also
satisfied while for q<0 there is a deviation from this relation confirming
behavior of markets for small fluctuations is affected by contribution of major
pair. For larger fluctuations, the cross-correlation contains information from
both local and global conditions. Width of singularity spectrum for
auto-correlation and cross-correlation are Δαxx∈[0.304,0.905] and Δαxy∈[0.246,1.178], respectively. The
wide range of singularity spectrum for cross-correlation confirms that the
bilateral relation between Stock markets is more complex. The value of
σDCCA indicates that all pairs of stock market studied in this time
interval belong to cross-correlated processes.Comment: 16 pages, 13 figures and 4 tables, major revision and match to
published versio