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A Closer Look at the Minimum-Variance Portfolio Optimization Model

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  • Zhifeng Dai

Abstract

Recently, by imposing the regularization term to objective function or additional norm constraint to portfolio weights, a number of alternative portfolio strategies have been proposed to improve the empirical performance of the minimum-variance portfolio. In this paper, we firstly examine the relation between the weight norm-constrained method and the objective function regularization method in minimum-variance problems by analyzing the Karush–Kuhn–Tucker conditions of their Lagrangian functions. We give the range of parameters for the two models and the corresponding relationship of parameters. Given the range and manner of parameter selection, it will help researchers and practitioners better understand and apply the relevant portfolio models. We apply these models to construct optimal portfolios and test the proposed propositions by employing real market data.

Suggested Citation

  • Zhifeng Dai, 2019. "A Closer Look at the Minimum-Variance Portfolio Optimization Model," Mathematical Problems in Engineering, Hindawi, vol. 2019, pages 1-8, August.
    • Handle: RePEc:hin:jnlmpe:1452762
    DOI: 10.1155/2019/1452762
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    Cited by:

    1. Vasilios N. Katsikis & Spyridon D. Mourtas & Predrag S. Stanimirović & Shuai Li & Xinwei Cao, 2021. "Time-Varying Mean-Variance Portfolio Selection under Transaction Costs and Cardinality Constraint Problem via Beetle Antennae Search Algorithm (BAS)," SN Operations Research Forum, Springer, vol. 2(2), pages 1-26, June.
    2. Spyridon D. Mourtas & Chrysostomos Kasimis, 2022. "Exploiting Mean-Variance Portfolio Optimization Problems through Zeroing Neural Networks," Mathematics, MDPI, vol. 10(17), pages 1-20, August.

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