Universität zu Köln

Reh, Laura ORCID: 0000-0002-8083-0535, Krueger, Fabian and Liesenfeld, Roman ORCID: 0000-0001-6996-6215 (2023). Predicting the Global Minimum Variance Portfolio. Journal of Business & Economic Statistics JBES, 41 (2). 440 -453. PHILADELPHIA: TAYLOR & FRANCIS. ISSN 1537-2707

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Abstract

We propose a novel dynamic approach to forecast the weights of the global minimum variance portfolio (GMVP) for the conditional covariance matrix of asset returns. The GMVP weights are the population coefficients of a linear regression of a benchmark return on a vector of return differences. This representation enables us to derive a consistent loss function from which we can infer the GMVP weights without imposing any distributional assumptions on the returns. In order to capture time variation in the returns' conditional covariance structure, we model the portfolio weights through a recursive least squares (RLS) scheme as well as by generalized autoregressive score (GAS) type dynamics. Sparse parameterizations and targeting toward the weights of the equally weighted portfolio ensure scalability with respect to the number of assets. We apply these models to daily stock returns, and find that they perform well compared to existing static and dynamic approaches in terms of both the expected loss and unconditional portfolio variance.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Reh, Laura UNSPECIFIED orcid.org/0000-0002-8083-0535 UNSPECIFIED Krueger, Fabian UNSPECIFIED UNSPECIFIED UNSPECIFIED Liesenfeld, Roman UNSPECIFIED orcid.org/0000-0001-6996-6215 UNSPECIFIED
URN:	urn:nbn:de:hbz:38-669032
DOI:	10.1080/07350015.2022.2035226
Journal or Publication Title:	Journal of Business & Economic Statistics JBES
Volume:	41
Number:	2
Page Range:	440 -453
Date:	2023
Publisher:	TAYLOR & FRANCIS
Place of Publication:	PHILADELPHIA
ISSN:	1537-2707
Language:	English
Faculty:	Faculty of Management, Economy and Social Sciences
Divisions:	Center of Excellence C-SEB
Subjects:	Economics
Uncontrolled Keywords:	Keywords Language SHRINKAGE ESTIMATION; PERFORMANCE; MODELS Multiple languages Economics; Social Sciences, Mathematical Methods; Statistics & Probability Multiple languages
Refereed:	Yes
URI:	http://kups.ub.uni-koeln.de/id/eprint/66903