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A test for the global minimum variance portfolio for small sample and singular covariance
Department of Mathematics, Stockholm University, Stockholm, Sweden.
Department of Mathematics, Aarhus University, Aarhus, Denmark.
Department of Statistics, Lund University, Lund, Sweden.
2017 (English)In: AStA Advances in Statistical Analysis, ISSN 1863-8171, E-ISSN 1863-818X, Vol. 101, no 3, p. 253-265Article in journal (Refereed) Published
Abstract [en]

Recently, a test dealing with the linear hypothesis for the global minimum variance portfolio weights was obtained under the assumption of non-singular covariance matrix. However, the problem of potential multicollinearity and correlations of assets constitutes a limitation of the classical portfolio theory. Therefore, there is an interest in developing theory in the presence of singularities in the covariance matrix. In this paper, we extend the test by analyzing the portfolio weights in the small sample case with a singular population covariance matrix. The results are illustrated using actual stock returns and a discussion of practical relevance of the model is presented. 

Place, publisher, year, edition, pages
Springer, 2017. Vol. 101, no 3, p. 253-265
Keywords [en]
Global minimum variance portfolio, Singular Wishart distribution, Singular covariance matrix, Small sample problem
National Category
Probability Theory and Statistics
Research subject
Mathematics
Identifiers
URN: urn:nbn:se:oru:diva-54838DOI: 10.1007/s10182-016-0282-zISI: 000406350700002Scopus ID: 2-s2.0-84995767761OAI: oai:DiVA.org:oru-54838DiVA, id: diva2:1066854
Available from: 2017-01-19 Created: 2017-01-19 Last updated: 2017-09-22Bibliographically approved

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