Detecting structural changes in large portfolios
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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.
KeywordsCorrelation Structural change Cluster analysis Portfolio management
JEL ClassificationC12 C55 C58 G11
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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