High Dimensional Covariance Matrix Estimation via Bayesian Method

DOI

10.2991/iceeim-14.2014.65How to use a DOI?

Keywords

covariance matrix estimation, portfolios, bayes, unbiasedness

Abstract

High-dimensional covariance matrix estimation and its applications in the portfolio selection are increasingly becoming an important topic. However, classical statistical methods used to estimate the sample covariance matrix will lead to inverse covariance matrix biased. Based on this, we propose the high dimensional covariance matrix estimation via Bayesian Method, to ensure that the result of the inverse covariance estimation is unbiased.

Copyright

© 2014, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Jie Tang
AU  - Si-ming Huang
PY  - 2014/04
DA  - 2014/04
TI  - High Dimensional Covariance Matrix Estimation via Bayesian Method
BT  - Proceedings of the 2014 International Conference on e-Education, e-Business and Information Management
PB  - Atlantis Press
SP  - 221
EP  - 224
SN  - 1951-6851
UR  - https://doi.org/10.2991/iceeim-14.2014.65
DO  - 10.2991/iceeim-14.2014.65
ID  - Tang2014/04
ER  -