基于横截面回归和Fama-MacBeth估计的鲁棒投资组合优化问题研究

doi:10.15960/j.cnki.issn.1007-6093.2021.03.008

摘要/Abstract

摘要： 考虑了不同于Goldfarb和Iyengar （2003）的因子模型，通过横截面回归分析以及Fama-MacBeth估计构造了关于资产的平均收益向量和协方差矩阵的不确定性集合（置信区域）。基于这些不确定性集合以及Markowitz“均值-方差模型”的鲁棒投资组合问题，提出了多个鲁棒投资组合问题，并对应的推导出其等价的半正定规划形式，使得问题可以在多项式时间内求解。

关键词: 鲁棒优化, 均值-方差模型, 横截面因子模型, 半正定规划

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

中图分类号:

F224.3
O224

江波, 朱喜华. 基于横截面回归和Fama-MacBeth估计的鲁棒投资组合优化问题研究[J]. 运筹学学报, 2021, 25(3): 133-146.

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.

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