Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/288436 
Authors: 
Year of Publication: 
2020
Citation: 
[Journal:] Mathematical Methods of Operations Research [ISSN:] 1432-5217 [Volume:] 92 [Issue:] 1 [Publisher:] Springer [Place:] Berlin, Heidelberg [Year:] 2020 [Pages:] 1-32
Publisher: 
Springer, Berlin, Heidelberg
Abstract: 
The best constant re-balanced portfolio represents the standard estimator for the log-optimal portfolio. It is shown that a quadratic approximation of log-returns works very well on a daily basis and a mean-variance estimator is proposed as an alternative to the best constant re-balanced portfolio. It can easily be computed and the numerical algorithm is very fast even if the number of dimensions is high. Some small-sample and the basic large-sample properties of the estimators are derived. The asymptotic results can be used for constructing hypothesis tests and for computing confidence regions. For this purpose, one should apply a finite-sample correction, which substantially improves the large-sample approximation. However, it is shown that the impact of estimation errors concerning the expected asset returns is serious. The given results confirm a general rule, which has become folklore during the last decades, namely that portfolio optimization typically fails on estimating expected asset returns.
Subjects: 
Best constant re-balanced portfolio
Estimation risk
Growth-optimal portfolio
Log-optimal portfolio
Mean-variance optimization
JEL: 
C13
G11
Persistent Identifier of the first edition: 
Creative Commons License: 
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Document Type: 
Article
Document Version: 
Published Version

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