Estimation of optimal portfolio compositions for Gaussian returns
Bodnar Taras and
Schmid Wolfgang
Additional contact information
Bodnar Taras: European University Viadrina, Department of Statistics, Frankfurt (Oder), Deutschland
Statistics & Risk Modeling, 2009, vol. 26, issue 3, 179-201
Abstract:
We consider the expected return and the variance of the expected quadratic utility portfolio and the tangency portfolio. The expected returns on the individual assets and their covariance matrix are estimated by the sample mean and the sample covariance matrix. Replacing the unknown parameters by these estimators in the portfolio characteristics estimators of the expected portfolio return and the portfolio variance are obtained.In this paper we calculate the densities of these estimators assuming independent and multivariate normally distributed returns. Because the densities can be computed by using standard mathematical software packages these representations are very useful. These results can be applied to construct tests and confidence intervals for the parameters of the efficient frontier.
Keywords: asset allocation; portfolio analysis; mean-variance portfolio; parameter uncertainty; portfolio characteristics (search for similar items in EconPapers)
Date: 2009
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)
Downloads: (external link)
https://doi.org/10.1524/stnd.2008.0918 (text/html)
For access to full text, subscription to the journal or payment for the individual article is required.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:bpj:strimo:v:26:y:2009:i:3:p:179-201:n:1
Ordering information: This journal article can be ordered from
https://www.degruyter.com/journal/key/strm/html
DOI: 10.1524/stnd.2008.0918
Access Statistics for this article
Statistics & Risk Modeling is currently edited by Robert Stelzer
More articles in Statistics & Risk Modeling from De Gruyter
Bibliographic data for series maintained by Peter Golla ().