Estimation of Large Precision Matrix for High Dimensional Mean-Variance Optimization

• Main Authors: Kuang, Hsien-Chi, 匡顯吉
• Other Authors: Wang, Hsiu-Ying
• Format: Others
• Language: en_US
• Published: 2016
• Online Access: http://ndltd.ncl.edu.tw/handle/59471158651126773787

碩士 === 國立交通大學 === 統計學研究所 === 104 === Recently, it has drawn attention on estimation of high-dimensional covariance matrices by using factor analysis. However, it is very difficult to apply factor analysis estimation of high-dimensional precision matrices. Because one of the commonly used conditions for estimating high-dimensional error precision matrix is to assume the covariance matrix to be sparse. This study combine modified Cholesky decomposition and orthogonal greedy algorithm (OGA) approaches to estimate the high-dimensional precision matrix under the constraint that the covariance matrix is sparse. The result can be used to deal with the mean-variance portfolio optimization problem. According to the simulation results, the proposed approach outperforms the adaptive thresholding method.