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Robustness of the inference procedures for the global minimum variance portfolio weights in a skew-normal model

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  • Taras Bodnar
  • Arjun K. Gupta

Abstract

In this paper, we study the influence of skewness on the distributional properties of the estimated weights of optimal portfolios and on the corresponding inference procedures derived for the optimal portfolio weights assuming that the asset returns are normally distributed. It is shown that even a simple form of skewness in the asset returns can dramatically influence the performance of the test on the structure of the global minimum variance portfolio. The results obtained can be applied in the small sample case as well. Moreover, we introduce an estimation procedure for the parameters of the skew-normal distribution that is based on the modified method of moments. A goodness-of-fit test for the matrix variate closed skew-normal distribution has also been derived. In the empirical study, we apply our results to real data of several stocks included in the Dow Jones index.

Suggested Citation

  • Taras Bodnar & Arjun K. Gupta, 2015. "Robustness of the inference procedures for the global minimum variance portfolio weights in a skew-normal model," The European Journal of Finance, Taylor & Francis Journals, vol. 21(13-14), pages 1176-1194, November.
    • Handle: RePEc:taf:eurjfi:v:21:y:2015:i:13-14:p:1176-1194
    DOI: 10.1080/1351847X.2012.696073
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    Cited by:

    1. Javed, Farrukh & Mazur, Stepan & Thorsén, Erik, 2021. "Tangency portfolio weights under a skew-normal model in small and large dimensions," Working Papers 2021:13, Örebro University, School of Business.
    2. Naderi, Mehrdad & Hashemi, Farzane & Bekker, Andriette & Jamalizadeh, Ahad, 2020. "Modeling right-skewed financial data streams: A likelihood inference based on the generalized Birnbaum–Saunders mixture model," Applied Mathematics and Computation, Elsevier, vol. 376(C).

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