On interrelations of recurrences and connectivity trends between stock indices

Abstract

Financial data has been extensively studied for correlations using Pearson’s cross-correlation coefficient as the point of departure. We employ an estimator based on recurrence plots — the correlation of probability of recurrence () — to analyze connections between nine stock indices spread worldwide. We suggest a slight modification of the approach in order to get more robust results. We examine trends in for an approximately 19-month window moved along the time series and compare them to trends in . Binning 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 during the dot-com bubble by shifting the time series to align their peaks. mainly uncovers that the markets move in and out of periods of strong connectivity erratically, instead of moving monotonically towards increasing global connectivity. This is in contrast to , which gives a picture of ever-increasing correlation. also exhibits that time-shifted markets have high connectivity around the dot-com bubble of 2000. We use significance tests using twin surrogates to interpret all the measures estimated in the study.