Considering the shortcomings of the traditional sample covariance matrix estimation, this paper proposes an improved global minimum variance portfolio model and named spectral corrected and regularized global minimum variance portfolio (SCRGMVP), which is better than the traditional risk model. The key of this method is that under the assumption that the population covariance matrix follows the spiked model and the method combines the design idea of the sample spectrally-corrected covariance matrix and regularized. The simulation of real and synthetic data shows that our method is not only better than the performance of traditional sample covariance matrix estimation (SCME), shrinkage estimation (SHRE), weighted shrinkage estimation (WSHRE) and simple spectral correction estimation (SCE), but also has lower computational complexity.

翻译：针对传统样本协方差矩阵估计的不足，本文提出一种改进的全局最小方差投资组合模型，命名为谱校正与正则化全局最小方差投资组合（SCRGMVP），该模型优于传统风险模型。该方法的核心在于，在假设总体协方差矩阵服从尖峰模型的前提下，融合了样本谱校正协方差矩阵的设计思想与正则化方法。真实数据和合成数据的仿真实验表明，我们的方法不仅优于传统样本协方差矩阵估计（SCME）、收缩估计（SHRE）、加权收缩估计（WSHRE）和简单谱校正估计（SCE）的性能，而且具有更低的计算复杂度。