  The financial markets are understood as complex dynamical systems whose
dynamics is analysed mostly using nonstationary and brief data sets that
usually come from stock markets. For such data sets, a reliable method of
analysis is based on recurrence plots and recurrence networks, constructed from
the data sets over the period of study. In this study, we do a comprehensive
analysis of the complexity of the underlying dynamics of 26 markets around the
globe using recurrence based measures. We also examine trends in the nature of
transitions as revealed from these measures by the sliding window analysis
along the time series during the global financial crisis of 2008 and compare
that with changes during the most recent pandemic related lock down. We show
that the measures derived from recurrence patterns can be used to capture the
nature of transitions in stock market dynamics. Our study reveals that the
changes around 2008 indicate stochasticity driven transition, which is
different from the transition during the pandemic.
