Universität zu Köln

Golosnoy, Vasyl and Gribisch, Bastian (2022). Modeling and forecasting realized portfolio weights. J. Bank Financ., 138. AMSTERDAM: ELSEVIER. ISSN 1872-6372

Full text not available from this repository.

Abstract

We propose direct multiple time series models for predicting high dimensional vectors of observable realized global minimum variance portfolio (GMVP) weights computed based on high-frequency intraday returns. We apply Lasso regression techniques, develop a class of multiple AR(FI)MA models for realized GMVP weights, suggest suitable model restrictions, propose M-type estimators and derive the statistical properties of these estimators. In the empirical analysis for portfolios of 225 stocks from the S&P 500 we find that our direct models effectively minimize either statistical or economic forecasting losses both in- and out-of-sample as compared to relevant alternative approaches. (c) 2022 Elsevier B.V. All rights reserved.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Golosnoy, Vasyl UNSPECIFIED UNSPECIFIED UNSPECIFIED Gribisch, Bastian UNSPECIFIED UNSPECIFIED UNSPECIFIED
URN:	urn:nbn:de:hbz:38-665030
DOI:	10.1016/j.jbankfin.2022.106404
Journal or Publication Title:	J. Bank Financ.
Volume:	138
Date:	2022
Publisher:	ELSEVIER
Place of Publication:	AMSTERDAM
ISSN:	1872-6372
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language COVARIANCE-MATRIX ESTIMATOR; ECONOMETRIC-ANALYSIS; VOLATILITY; SELECTION; MARKET; PERFORMANCE; ALLOCATION; REGRESSION; SHRINKAGE; RETURN Multiple languages Business, Finance; Economics Multiple languages
URI:	http://kups.ub.uni-koeln.de/id/eprint/66503