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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.ORCID-id: 0000-0001-7855-8221
Bowling Green State University, USA.
2015 (engelsk)Inngår i: European Journal of Finance, ISSN 1351-847X, E-ISSN 1466-4364, Vol. 21, nr 13-14, s. 1176-1194Artikkel i tidsskrift (Fagfellevurdert) 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.

sted, utgiver, år, opplag, sider
2015. Vol. 21, nr 13-14, s. 1176-1194
Emneord [en]
asset pricing, parameter uncertainty, matrix variate skew-normal distribution, global minimum variance portfolio, statistical inference procedures
HSV kategori
Forskningsprogram
statistik
Identifikatorer
URN: urn:nbn:se:su:diva-127169DOI: 10.1080/1351847X.2012.696073OAI: oai:DiVA.org:su-127169DiVA, id: diva2:907183
Tilgjengelig fra: 2016-02-26 Laget: 2016-02-26 Sist oppdatert: 2020-03-05bibliografisk kontrollert

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