Universität zu Köln

Glombek, Konstantin (2014). Statistical Inference for High-Dimensional Global Minimum Variance Portfolios. Scand. J. Stat., 41 (4). S. 845 - 866. HOBOKEN: WILEY-BLACKWELL. ISSN 1467-9469

Full text not available from this repository.

Abstract

Many studies demonstrate that inference for the parameters arising in portfolio optimization often fails. The recent literature shows that this phenomenon is mainly due to a high-dimensional asset universe. Typically, such a universe refers to the asymptotics that the sample size n+1 and the sample dimension d both go to infinity while d/nc(0,1). In this paper, we analyze the estimators for the excess returns' mean and variance, the weights and the Sharpe ratio of the global minimum variance portfolio under these asymptotics concerning consistency and asymptotic distribution. Problems for stating hypotheses in high dimension are also discussed. The applicability of the results is demonstrated by an empirical study. Copyright (c) 2014 John Wiley & Sons, Ltd.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Glombek, Konstantin UNSPECIFIED UNSPECIFIED UNSPECIFIED
URN:	urn:nbn:de:hbz:38-422188
DOI:	10.1111/sjos.12066
Journal or Publication Title:	Scand. J. Stat.
Volume:	41
Number:	4
Page Range:	S. 845 - 866
Date:	2014
Publisher:	WILEY-BLACKWELL
Place of Publication:	HOBOKEN
ISSN:	1467-9469
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language COVARIANCE-MATRIX; INVERSE WISHART; SELECTION Multiple languages Statistics & Probability Multiple languages
URI:	http://kups.ub.uni-koeln.de/id/eprint/42218