High dimensional mean-variance optimization through factor analysis

Please use this identifier to cite or link to this item: https://ir.lib.ncu.edu.tw/handle/987654321/105814

Title:	High dimensional mean-variance optimization through factor analysis
Authors:	黃士峰;Chen, Binbin;Huang, Shih-Feng;Pan, Guangming
Contributors:	理學院統計研究所
Keywords:	Bootstrap method;Discriminant analysis;Estimating techniques;Factor model;Mathematical problems;Mean–variance optimization;Optimal portfolio allocation;Optimization techniques;Studies
Date:	2015-01-01
Issue Date:	2026-04-23 12:54:56 (UTC+8)
Publisher:	Academic Press Inc.;New York: Elsevier Inc
Abstract:	摘要： A factor analysis-based approach for estimating high dimensional covariance matrix is proposed and is applied to solve the mean–variance portfolio optimization problem in finance. The consistency of the proposed estimator is established by imposing a factor model structure with a relative weak assumption on the relationship between the dimension and the sample size. Numerical results indicate that the proposed estimator outperforms the plug-in, linear shrinkage and bootstrap-corrected approaches. 出版者： New York: Elsevier Inc 出版日期： 2015-01 出處： Journal of multivariate analysis, 2015-01, Vol.133, p.140-159 資源來源： Elsevier ScienceDirect Journals Complete 版權： 2014 Elsevier Inc. 版權： Copyright Taylor & Francis Group Jan 2015 識別號： ISSN: 0047-259X 識別號： EISSN: 1095-7243 識別號： DOI: 10.1016/j.jmva.2014.09.006 識別號： CODEN: JMVAAI
Appears in Collections:	[Graduate Institute of Statistics] journal & Dissertation

Files in This Item:

File	Description	Size	Format
index.html		0Kb	HTML	27	View/Open

社群 sharing