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Covariances matrix under the multivariate-Gh funtion to desing portfolios

Author

Listed:
  • José Antonio Núñez Mora

    (EGADE, Business School, Tecnologico de Monterrey, México)

  • Leovardo Mata Mata

    (EGADE, Business School, Tecnologico de Monterrey, México)

Abstract

In this paper we developed the estimation implementation of the generalized hyperbolic multivariate (GH) distribution with a non-fixed Bessel function. The covariance matrix estimated through the GH distribution complements the use of the Markowitz procedure to construct an efficient portfolio and reduce the variation coefficient of the expected return. The data are from the Stockholm index 30 from January 2010 to April 2014.

Suggested Citation

  • José Antonio Núñez Mora & Leovardo Mata Mata, 2016. "Covariances matrix under the multivariate-Gh funtion to desing portfolios," Contaduría y Administración, Accounting and Management, vol. 61(3), pages 535-550, Julio-Sep.
    • Handle: RePEc:nax:conyad:v:61:y:2016:i:3:p:535-550
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    References listed on IDEAS

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    1. Michael McAssey, 2013. "An empirical goodness-of-fit test for multivariate distributions," Journal of Applied Statistics, Taylor & Francis Journals, vol. 40(5), pages 1120-1131.
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    More about this item

    Keywords

    Expectation-maximization algorithm; Generalized hyperbolic distribution; Markowitz portfolio; Covariance matrix;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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