Abstract
We propose a novel methodology for constructing optimal portfolios in the presence of (i) model parameter uncertainty and (ii) user-specified constraints on the portfolio weights. This is a challenging problem, in large part because the constraint conditions generally preclude the derivation of closed-form solutions even in the absence of parameter uncertainty. Yet, in this article, we succeed in producing a practical solution, which is based on a herein proposed technique that we call a “perturbation method.” The method relies on a specially devised resampling procedure, whose performance is shown in simulations to compare favorably to other methods from the literature on portfolio optimization.
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Bennett, C.J., Zitikis, R. Estimation of Optimal Portfolio Weights Under Parameter Uncertainty and User-Specified Constraints: A Perturbation Method. J Stat Theory Pract 8, 423–438 (2014). https://doi.org/10.1080/15598608.2013.795125
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DOI: https://doi.org/10.1080/15598608.2013.795125