Efficient Multi-Change Point Analysis to Decode Economic Crisis Information from the S&P500 Mean Market Correlation
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 signifi...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-08-01
|
| Series: | Entropy |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1099-4300/25/9/1265 |
| _version_ | 1827726247054016512 |
|---|---|
| author | Martin Heßler Tobias Wand Oliver Kamps |
| author_facet | Martin Heßler Tobias Wand Oliver Kamps |
| author_sort | Martin Heßler |
| collection | DOAJ |
| description | 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 <i>S&P500</i> 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. |
| first_indexed | 2024-03-10T22:47:22Z |
| format | Article |
| id | doaj.art-7afc3ca881ba408fb47695b0b31c381f |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-10T22:47:22Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-7afc3ca881ba408fb47695b0b31c381f2023-11-19T10:35:13ZengMDPI AGEntropy1099-43002023-08-01259126510.3390/e25091265Efficient Multi-Change Point Analysis to Decode Economic Crisis Information from the S&P500 Mean Market CorrelationMartin Heßler0Tobias Wand1Oliver Kamps2Institute for Theoretical Physics, University of Münster, Wilhelm-Klemm-Straße 9, 48149 Münster, GermanyInstitute for Theoretical Physics, University of Münster, Wilhelm-Klemm-Straße 9, 48149 Münster, GermanyCenter for Nonlinear Science, University of Münster, Corrensstraße 2, 48149 Münster, GermanyIdentifying 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 <i>S&P500</i> 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.https://www.mdpi.com/1099-4300/25/9/1265Bayesian multi-change point analysislinear trend segment fitcomputationally efficient open-source python implementation<i>S&P500</i>mean market correlationmarket mode |
| spellingShingle | Martin Heßler Tobias Wand Oliver Kamps Efficient Multi-Change Point Analysis to Decode Economic Crisis Information from the S&P500 Mean Market Correlation Entropy Bayesian multi-change point analysis linear trend segment fit computationally efficient open-source python implementation <i>S&P500</i> mean market correlation market mode |
| title | Efficient Multi-Change Point Analysis to Decode Economic Crisis Information from the S&P500 Mean Market Correlation |
| title_full | Efficient Multi-Change Point Analysis to Decode Economic Crisis Information from the S&P500 Mean Market Correlation |
| title_fullStr | Efficient Multi-Change Point Analysis to Decode Economic Crisis Information from the S&P500 Mean Market Correlation |
| title_full_unstemmed | Efficient Multi-Change Point Analysis to Decode Economic Crisis Information from the S&P500 Mean Market Correlation |
| title_short | Efficient Multi-Change Point Analysis to Decode Economic Crisis Information from the S&P500 Mean Market Correlation |
| title_sort | efficient multi change point analysis to decode economic crisis information from the s p500 mean market correlation |
| topic | Bayesian multi-change point analysis linear trend segment fit computationally efficient open-source python implementation <i>S&P500</i> mean market correlation market mode |
| url | https://www.mdpi.com/1099-4300/25/9/1265 |
| work_keys_str_mv | AT martinheßler efficientmultichangepointanalysistodecodeeconomiccrisisinformationfromthesp500meanmarketcorrelation AT tobiaswand efficientmultichangepointanalysistodecodeeconomiccrisisinformationfromthesp500meanmarketcorrelation AT oliverkamps efficientmultichangepointanalysistodecodeeconomiccrisisinformationfromthesp500meanmarketcorrelation |