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Author
Al Wakil Anmar (University of Paris-Est, France)
Title
A Probabilistic-Based Portfolio Resampling Under the Mean-Variance Criterion
Source
Econometric Research in Finance, 2021, vol. 6, nr 1, s. 45-56, rys., wykr., bibliogr. 17 poz.
Keyword
Metody portfelowe, Teoria portfelowa Markowitza, Estymacja
Portfolio methods, Markowitz portfolio theory, Estimation
Note
JEL classification: G11, C14, C15
summ.
Abstract
An abundant amount of literature has documented the limitations of traditional unconstrained mean-variance optimization and Efficient Frontier (EF) considered as an estimation-error maximization that magnifies errors in parameter estimates. Originally introduced by Michaud (1998), empirical superiority of portfolio resampling supposedly lies in the addressing of parameter uncertainty by averaging forecasts that are based on a large number of bootstrap replications. Nevertheless, averaging over resampled portfolio weights in order to obtain the unique Resampled Efficient Frontier (REF, U.S. patent number 6,003,018) has been documented as a debated statistical procedure. Alternatively, we propose a probabilistic extension of the Michaud resampling that we introduce as the Probabilistic Resampled Efficient Frontier (PREF). The originality of this work lies in addressing the information loss in the REF by proposing a geometrical three-dimensional representation of the PREF in the mean-variance-probability space. Interestingly, this geometrical representation illustrates a confidence region around the naive EF associated to higher probabilities; in particular for simulated Global-Mean-Variance portfolios. Furthermore, the confidence region becomes wider with portfolio return, as is illustrated by the dispersion of simulated Maximum-Mean portfolios.(original abstract)
Accessibility
The Library of Warsaw School of Economics
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Bibliography
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Cited by
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ISSN
2451-1935
2451-2370
Language
eng
URI / DOI
https://doi.org/10.2478/erfin-2021-0003
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