Modeling and forecasting realized portfolio weights

Vasyl Golosnoy and Bastian Gribisch

Journal of Banking & Finance, 2022, vol. 138, issue C

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.

Keywords: M-estimation; Lasso; Realized covariances; Realized GMVP; VARFIMA (search for similar items in EconPapers)
JEL-codes: C32 C58 G11 G17 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:jbfina:v:138:y:2022:i:c:s0378426622000048

DOI: 10.1016/j.jbankfin.2022.106404

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