  Identifying macroeconomic events that are responsible for dramatic changes of
economy is of particular relevance to understand the overall economic dynamics.
We introduce an open-source available efficient Python implementation of a
Bayesian multi-trend change point analysis which solves significant memory and
computing time limitations to extract crisis information from a correlation
metric. Therefore, we focus on the recently investigated S&P500 mean market
correlation in a period of roughly 20 years that includes the dot-com bubble,
the global financial crisis and the Euro crisis. The analysis is performed
two-fold: first, in retrospect on the whole dataset and second, in an on-line
adaptive manner in pre-crisis segments. The on-line sensitivity horizon is
roughly determined to be 80 up to 100 trading days after a crisis onset. A
detailed comparison to global economic events supports the interpretation of
the mean market correlation as an informative macroeconomic measure by a rather
good agreement of change point distributions and major crisis events.
Furthermore, the results hint to the importance of the U.S. housing bubble as
trigger of the global financial crisis, provide new evidence for the general
reasoning of locally (meta)stable economic states and could work as a
comparative impact rating of specific economic events.

Statistical Finance;Computational Finance