IDEAS home Printed from https://ideas.repec.org/a/cup/jfinqa/v12y1977i03p347-361_02.html
   My bibliography  Save this article

Mean-Variance Portfolio Selection with Either a Singular or Nonsingular Variance-Covariance Matrix

Author

Listed:
  • Buser, Stephen A.

Abstract

In derivations of the mean-variance model of portfolio selection, authors from Markowitz [6 and 7] and Tobin [11] to Merton [8] and Black [1] rely on the inverse of the matrix of variances and covariances for the returns on risky securities. Unfortunately, as is shown in this paper, such an inverse does not exist when risk-free combinations can be formed from the risky securities. Accordingly, the general validity of the mean-variance model is challenged by the existence of opportunities for hedging, including those fostered by short sales and the rapidly expanding markets for warrants, options, and futures. Fortunately, the mean-variance model is tractable even when the conventional methods for deriving it fail. Alternative solution procedures presented in this paper are valid with or without riskless securities and with either singular or nonsingular variance-covariance matrices. The important properties of the mean-variance model are shown to extend for the previously omitted cases. In particular, the frontier of mean-variance combinations is always well-defined, is always strictly convex, and (the efficient portion of the frontier) is always positively sloped. In addition, the frontier of mean-variance combinations always can be expressed in terms of a pair of mutual funds which are determined on purely technical grounds.

Suggested Citation

  • Buser, Stephen A., 1977. "Mean-Variance Portfolio Selection with Either a Singular or Nonsingular Variance-Covariance Matrix," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 12(3), pages 347-361, September.
  • Handle: RePEc:cup:jfinqa:v:12:y:1977:i:03:p:347-361_02
    as

    Download full text from publisher

    File URL: https://www.cambridge.org/core/product/identifier/S0022109000023024/type/journal_article
    File Function: link to article abstract page
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ni Zhan & Yijia Sun & Aman Jakhar & He Liu, 2021. "Graphical Models for Financial Time Series and Portfolio Selection," Papers 2101.09214, arXiv.org.
    2. Jiawen Xu & Yixuan Li & Kai Liu & Tao Chen, 2023. "Portfolio selection: from under-diversification to concentration," Empirical Economics, Springer, vol. 64(4), pages 1539-1557, April.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:cup:jfinqa:v:12:y:1977:i:03:p:347-361_02. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Kirk Stebbing (email available below). General contact details of provider: https://www.cambridge.org/jfq .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.