  Monitoring economic conditions and financial stability with an early warning
system serves as a prevention mechanism for unexpected economic events. In this
paper, we investigate the statistical performance of sequential break-point
detectors for stationary time series regression models with extensive
simulation experiments. We employ an online sequential scheme for monitoring
economic indicators from the European as well as the American financial markets
that span the period during the 2008 financial crisis. Our results show that
the performance of these tests applied to stationary time series regressions
such as the AR(1) as well as the AR(1)-GARCH(1,1) depend on the severity of the
break as well as the location of the break-point within the out-of-sample
period. Consequently, our study provides some useful insights to practitioners
for sequential break-point detection in economic and financial conditions.
