Statistical inferences for realized portfolio weights

Vasyl Golosnoy, Wolfgang Schmid, Miriam Isabel Seifert and Taras Lazariv

Econometrics and Statistics, 2020, vol. 14, issue C, 49-62

Abstract: Statistical inferences for weights of the global minimum variance portfolio (GMVP) are of both theoretical and practical relevance for mean-variance portfolio selection. Daily realized GMVP weights depend only on realized covariance matrix computed from intraday high-frequency returns. Both finite sample and asymptotic distributional properties of the realized GMVP weights are deduced. Then, statistical tests for the GMVP proportions are developed in order to provide sequential monitoring with on-line decisions whether a given portfolio composition deviates from the current GMVP significantly. The theoretical results are illustrated both in Monte Carlo simulations and in an empirical application.

Keywords: Minimum variance portfolio; Realized covariance matrix; Structural change; Control charts; Tests for portfolio weights (search for similar items in EconPapers)
JEL-codes: C13 C40 C58 G01 G11 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S2452306218300534
Full text for ScienceDirect subscribers only. Contains open access articles

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:ecosta:v:14:y:2020:i:c:p:49-62

DOI: 10.1016/j.ecosta.2018.08.003

Access Statistics for this article

Econometrics and Statistics is currently edited by E.J. Kontoghiorghes, H. Van Dijk and A.M. Colubi

More articles in Econometrics and Statistics from Elsevier
Bibliographic data for series maintained by Catherine Liu ().