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

• Published in: Entropy (Basel, Switzerland) Vol. 25; no. 9; p. 1265
• Main Authors: Martin Heßler, Tobias Wand, Oliver Kamps
• Format: Journal Article
• Language: English
• Published: MDPI AG 01.08.2023
• Subjects: Bayesian multi-change point analysis; computationally efficient open-source python implementation; linear trend segment fit; market mode; mean market correlation; S&P500
• ISSN: 1099-4300
• DOI: 10.3390/e25091265

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 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 new evidence for the general reasoning of locally (meta)stable economic states, and could work as a comparative impact rating of specific economic events.