High Dimensional Global Minimum Variance Portfolio

7 Pages Posted: 27 Aug 2015

See all articles by Li Hua

Li Hua

Changchun University - Department of Science

Bai Zhidong

Hong Kong Baptist University (HKBU) – Department of Accountancy and Law - Department of Economics

Wing-Keung Wong

Asia University, Department of Finance

Date Written: August 26, 2015

Abstract

This paper proposes the spectral corrected methodology to estimate the Global Minimum Variance Portfolio (GMVP) for the high dimensional data. In this paper, we analysis the limiting properties of the spectral corrected GMVP estimator as the dimension and the number of the sample set increase to infinity proportionally. In addition, we compare the spectral corrected estimation with the linear shrinkage and nonlinear shrinkage estimations and obtain that the performance of the spectral corrected methodology is best in the simulation study.

Keywords: Global Minimum Variance Portfolio, Spectral Corrected Covariance, Sample Covariance

JEL Classification: G11; C13

Suggested Citation

Hua, Li and Zhidong, Bai and Wong, Wing-Keung, High Dimensional Global Minimum Variance Portfolio (August 26, 2015). Available at SSRN: https://ssrn.com/abstract=2650981 or http://dx.doi.org/10.2139/ssrn.2650981

Li Hua

Changchun University - Department of Science ( email )

6543 Satellite Road
Chaoyang District
Changchun City, Jilin Province
China

Bai Zhidong

Hong Kong Baptist University (HKBU) – Department of Accountancy and Law - Department of Economics ( email )

Hong Kong

Wing-Keung Wong (Contact Author)

Asia University, Department of Finance ( email )

Taiwan
Taiwan

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