Efficient Multi-Change Point Analysis to Decode Economic Crisis Information from the S&P500 Mean Market Correlation

Efficient Multi-Change Point Analysis to Decode Economic Crisis Information from the S&P500 Mean Market Correlation

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Identifying macroeconomic events that are responsible for dramatic changes of economy is of particular relevance to understanding 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 google pixel 7 freedom 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 online adaptive manner in pre-crisis segments.The online 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 at the importance of the U.S.housing bubble as a trigger of the global financial crisis, provide u11-200ps new evidence for the general reasoning of locally (meta)stable economic states, and could work as a comparative impact rating of specific economic events.