  Financial data has been extensively studied for correlations using Pearson's
cross-correlation coefficient {\rho} as the point of departure. We employ an
estimator based on recurrence plots --- the Correlation of Probability of
Recurrence (CPR) --- to analyze connections between nine stock indices spread
worldwide. We suggest a slight modification of the CPR approach in order to get
more robust results. We examine trends in CPR for an approximately 19-month
window moved along the time series and compare them to {\rho}. Binning CPR into
three levels of connectedness: strong, moderate and weak, we extract the trends
in number of connections in each bin over time. We also look at the behavior of
CPR during the Dot-Com bubble by shifting the time series to align their peaks.
CPR mainly uncovers that the markets move in and out of periods of strong
connectivity erratically, instead of moving monotonously towards increasing
global connectivity. This is in contrast to {\rho}, which gives a picture of
ever increasing correlation. CPR also exhibits that time shifted markets have
high connectivity around the Dot-Com bubble of 2000. We stress on the
importance of significance testing in interpreting measures applied to field
data. CPR is more robust to significance testing. It has the additional
advantages of being robust to noise, and reliable for short time series lengths
and low frequency of sampling. Further, it is more sensitive to changes than
{\rho} as it captures correlations between the essential dynamics of the
underlying systems.
