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.
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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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Posch, P.N., Ullmann, D. & Wied, D. Detecting structural changes in large portfolios. Empir Econ 56, 1341–1357 (2019). https://doi.org/10.1007/s00181-017-1392-5
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DOI: https://doi.org/10.1007/s00181-017-1392-5