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运筹学学报 >

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

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

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

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  • 1. 上海财经大学信息管理与工程学院, 上海 200433;
    2. 上海财经大学交叉科学研究院, 上海 200433

收稿日期: 2021-03-24

  网络出版日期: 2021-09-26

基金资助

国家自然科学基金（Nos.11771269，11831002），上海财经大学研究生创新基金（No.CXJJ-2019-391）

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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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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

本文引用格式

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

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

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