A completely automated optimization strategy for global minimum-variance portfolios based on a new test for structural breaks
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Date
2013-05-13
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Abstract
We present a completely automated optimization strategy which combines the classical
Markowitz mean-variance portfolio theory with a recently proposed test for structural breaks in co-
variance matrices. With respect to equity portfolios, global minimum-variance optimizations, which base
solely on the covariance matrix, yield considerable results in previous studies. However, nancial assets
cannot be assumed to have a constant covariance matrix over longer periods of time. Hence, we estimate the covariance matrix of the assets by respecting potential change points. The resulting approach
resolves issues like timing or determining a sample for parameter estimation. Moreover, we apply the
approach to two datasets and compare the results to relevant benchmark techniques by means of an
out-of-sample study. It is shown that the new approach outperforms equally weighted portfolios and
plain minimum-variance portfolios on average.
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Keywords
fluctuation test, portfolio optimization, structural break