Stable Portfolio Selection Strategy for Mean-Variance-CVaR Model under High-Dimensional Scenarios

This paper aims to study stable portfolios with mean-variance-CVaR criteria for high-dimensional data. Combining different estimators of covariance matrix, computational methods of CVaR, and regularization methods, we construct five progressive optimization problems with short selling allowed. The impacts of different methods on out-of-sample performance of portfolios are compared. Results show that the optimization model with well-conditioned and sparse covariance estimator, quantile regression computational method for CVaR, and reweightedL1norm performs best, which serves for stabilizing the out-of-sample performance of the solution and also encourages a sparse portfolio.