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Operations Research Transactions >

2021 , Vol. 25 >Issue 3: 133 - 146

DOI: https://doi.org/10.15960/j.cnki.issn.1007-6093.2021.03.008

Robust portfolio selection based on cross-sectional regression and Fama-Macbeth estimator

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  • 1. School of Information Management and Engineering, Shanghai University of Finance and Economics, Shanghai 200433, China;
    2. Research Institute for Interdisciplinary Sciences, Shanghai University of Finance and Economics, Shanghai 200433, China

Received date: 2021-03-24

  Online published: 2021-09-26

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Abstract

This paper considers a factor model different from Goldfarb and Iyengar (2003) and the uncertainty set for the mean profit vector and covariance matrix of the assets in the robust problems are constructed by the cross-sectional regression and Fama-MacBeth estimator. Based on the robust portfolio selection problems under the Markowitz mean-variance model and these uncertainty sets, we prosed multiple robust portfolio selection problems and prove that these problems can be re-written as Semidefinite programmings which are computationally tractable.

Key words： robust optimization; mean-variance model; cross-section regression; Semidefinite programming

Cite this article

JIANG Bo, ZHU Xihua . Robust portfolio selection based on cross-sectional regression and Fama-Macbeth estimator[J]. Operations Research Transactions, 2021 , 25(3) : 133 -146 . DOI: 10.15960/j.cnki.issn.1007-6093.2021.03.008

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