  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.
