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Related Concept Videos

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Microsoft Excel: Pearson's Correlation

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Microsoft Excel is a powerful tool for statistical analysis, including calculating Pearson's correlation coefficient, which measures the strength and direction of a linear relationship between two continuous variables. Pearson's correlation coefficient, often denoted as "r," ranges from -1 to 1. A value close to 1 indicates a strong positive correlation, meaning as one variable increases, the other does too. A value close to -1 indicates a strong negative correlation, implying...
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Dimensional analysis simplifies complex physical problems and guides experimental investigations, but it does not provide complete solutions. It identifies the dimensionless groups that influence a phenomenon, but experimental data is needed to establish the specific relationships and validate theoretical predictions.
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Efficient Multi-Change Point Analysis to Decode Economic Crisis Information from the S&P500 Mean Market Correlation.

Martin Heßler1,2, Tobias Wand1,2, Oliver Kamps2

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This study introduces a Python tool for Bayesian change point analysis to identify economic crises using S&P500 correlations. The analysis reveals crisis onset within 80-100 days, highlighting the U.S. housing bubble

Keywords:
Bayesian multi-change point analysisS&P500computationally efficient open-source python implementationeconomic criseseconophysicslinear trend segment fitmarket factormarket modemean market correlation

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Area of Science:

  • Quantitative Finance
  • Econometrics
  • Computational Economics

Background:

  • Understanding macroeconomic events is crucial for economic dynamics.
  • Market correlation analysis offers insights into economic stability.
  • Previous methods faced limitations in speed and memory for crisis data extraction.

Purpose of the Study:

  • To develop an efficient Python implementation of Bayesian multi-trend change point analysis.
  • To extract crisis information from S&P500 mean market correlation.
  • To analyze economic dynamics during major crises like the dot-com bubble and global financial crisis.

Main Methods:

  • Bayesian multi-trend change point analysis.
  • Open-source Python implementation addressing memory and computing time constraints.
  • Retrospective and online adaptive analysis of S&P500 mean market correlation over 20 years.

Main Results:

  • Identified change points in market correlation align well with major global economic events.
  • Determined an online sensitivity horizon of 80-100 trading days post-crisis onset.
  • The U.S. housing bubble is suggested as a trigger for the global financial crisis.

Conclusions:

  • Mean market correlation serves as an informative macroeconomic measure.
  • The analysis provides evidence for locally (meta)stable economic states.
  • The methodology can be used for comparative impact ratings of economic events.