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

ArXiv ID: 2308.00087 “View on arXiv”

Authors: Unknown

Abstract

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.

Keywords: Bayesian Multi-trend Change Point Analysis, Correlation Metric, S&P500 Market Correlation, Non-Markovianity, Crisis Detection, Equities

Complexity vs Empirical Score

• Math Complexity: 8.0/10
• Empirical Rigor: 5.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced Bayesian change point analysis with significant mathematical derivations, yet also provides a specific open-source Python implementation and detailed backtesting on historical S&P500 data, demonstrating substantial empirical rigor.

  flowchart TD
    A["Research Goal: Efficiently detect economic crises<br/>from S&P 500 correlation structures"] --> B["Data Input<br/>S&P 500 Mean Market Correlation (20 Years)"]
    B --> C["Methodology<br/>Bayesian Multi-trend Change Point Analysis"]
    C --> D["Computational Process<br/>Online Adaptive Analysis<br/>(Pre-crisis segments)"]
    D --> E["Key Findings<br/>High sensitivity (80-100 trading days)<br/>Validates correlation as macroeconomic indicator<br/>U.S. housing bubble as primary trigger for GFC"]