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Robustness of the Inference Procedures for the Global Minimum Variance Portfolio Weights in a Skew Normal Model
European University Viadrina, Germany.
Bowling Green State University, USA.
2015 (English)In: European Journal of Finance, ISSN 1351-847X, E-ISSN 1466-4364, Vol. 21, no 13-14, 1176-1194 p.Article in journal (Refereed) Published
Abstract [en]

In this paper, we study the influence of skewness on the distributional properties of the estimated weightsof optimal portfolios and on the corresponding inference procedures derived for the optimal portfolioweights assuming that the asset returns are normally distributed. It is shown that even a simple form ofskewness in the asset returns can dramatically influence the performance of the test on the structure of theglobal 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 isbased on the modified method of moments.A goodness-of-fit test for the matrix variate closed skew-normaldistribution has also been derived. In the empirical study, we apply our results to real data of several stocksincluded in the Dow Jones index.

Place, publisher, year, edition, pages
2015. Vol. 21, no 13-14, 1176-1194 p.
Keyword [en]
asset pricing, parameter uncertainty, matrix variate skew-normal distribution, global minimum variance portfolio, statistical inference procedures
National Category
Economics and Business Probability Theory and Statistics
Research subject
Statistics
Identifiers
URN: urn:nbn:se:su:diva-127169DOI: 10.1080/1351847X.2012.696073OAI: oai:DiVA.org:su-127169DiVA: diva2:907183
Available from: 2016-02-26 Created: 2016-02-26 Last updated: 2016-03-03Bibliographically approved

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