Detecting structural changes in large portfolios

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Abstract

Model-free tests for constant parameters often fail to detect structural changes in high dimensions. In practice, this corresponds to a portfolio with many assets and a reasonable long time series. We reduce the dimensionality of the problem by looking at a compressed panel of time series obtained by cluster analysis and the principal components of the data. With this procedure, we can extend tests for constant correlation matrix from a sub-portfolio to whole indices, which we exemplify using a major stock index.

Keywords

Correlation Structural change Cluster analysis Portfolio management 

JEL Classification

C12 C55 C58 G11 

Notes

Acknowledgements

Financial support by the Collaborative Research Center Statistical Modeling of Nonlinear Dynamic Processes (SFB 823) of the German Research Foundation (DFG) is gratefully acknowledged.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Faculty of Business and EconomicsTU Dortmund UniversityDortmundGermany
  2. 2.Faculty of Business, Economics and Social ScienceUniversity of CologneCologneGermany

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