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Testing and dating structural changes in copula-based dependence measures.

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Summary
This summary is machine-generated.

This study introduces a new test for structural breaks in multivariate time series dependence. The method uses a bootstrap procedure and confidence intervals to accurately detect and date changes in financial data.

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Area of Science:

  • Econometrics
  • Time Series Analysis
  • Statistical Modeling

Background:

  • Accurate detection of structural breaks in multivariate time series is crucial for financial modeling.
  • Existing methods may lack precision in identifying and dating changes in dependence structures.
  • Copula-based dependence measures offer robust ways to analyze complex financial relationships.

Purpose of the Study:

  • To develop and validate a novel testing and dating procedure for structural breaks in the dependence structure of multivariate time series.
  • To assess the performance of a cumulative sum (CUSUM) type test using copula-based dependence measures.
  • To provide a reliable method for identifying breakpoint locations and confirming break events.

Main Methods:

  • Utilizing a CUSUM type test for constant copula-based dependence measures (Spearman's rank correlation, quantile dependencies).
  • Employing an i.i.d. bootstrap procedure to estimate critical values for the test's asymptotic null distribution.
  • Developing a pivot confidence interval procedure for dating breakpoints and assessing the significance of estimated break locations.

Main Results:

  • The simulation study demonstrated the size and power properties of the test under various dependence settings, including skewed and fat-tailed distributions.
  • The proposed pivot confidence interval procedure effectively dates breakpoints and distinguishes between separate break events.
  • The test was successfully applied to historical financial data, revealing insights during a major financial crisis.

Conclusions:

  • The developed CUSUM type test and pivot confidence interval procedure offer a robust framework for analyzing structural breaks in multivariate time series dependence.
  • The methodology is effective even with complex data distributions, showing good performance in simulations.
  • The application to financial data highlights the practical utility of the approach in understanding market dynamics during crises.