What really happens if the positive definiteness requirement on the covariance matrix of returns is relaxed in efficient portfolio selection?

Clarence C. Y. Kwan ()
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Clarence C. Y. Kwan: McMaster University

Financial Markets and Portfolio Management, 2018, vol. 32, issue 1, No 4, 77-110

Abstract: Abstract The Markowitz critical line method for mean–variance portfolio construction has remained highly influential today, since its introduction to the finance world six decades ago. The Markowitz algorithm is so versatile and computationally efficient that it can accommodate any number of linear constraints in addition to full allocations of investment funds and disallowance of short sales. For the Markowitz algorithm to work, the covariance matrix of returns, which is positive semi-definite, need not be positive definite. As a positive semi-definite matrix may not be invertible, it is intriguing that the Markowitz algorithm always works, although matrix inversion is required in each step of the iterative procedure involved. By examining some relevant algebraic features in the Markowitz algorithm, this paper is able to identify and explain intuitively the consequences of relaxing the positive definiteness requirement, as well as drawing some implications from the perspective of portfolio diversification. For the examination, the sample covariance matrix is based on insufficient return observations and is thus positive semi-definite but not positive definite. The results of the examination can facilitate a better understanding of the inner workings of the highly sophisticated Markowitz approach by the many investors who use it as a tool to assist portfolio decisions and by the many students who are introduced pedagogically to its special cases.

Keywords: Mean–variance efficient portfolios; Markowitz critical line method; Positive definite matrix; Positive semi-definite matrix (search for similar items in EconPapers)
JEL-codes: C61 G11 (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/s11408-018-0306-7

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