Exponential smoothing of realized portfolio weights

Vasyl Golosnoy, Bastian Gribisch and Miriam Isabel Seifert

Journal of Empirical Finance, 2019, vol. 53, issue C, 222-237

Abstract: The model-free exponential smoothing (ES) approach is a simple and robust way to make forecasts of random vectors. In this paper we investigate ES predictors for weights of high-dimensional realized global minimum variance portfolio (GMVP) which depend only on a realized covariance matrix of financial risky assets. We contrast a direct ES prediction of realized GMVP proportions and an indirect ES forecast, where smoothing is applied to realized covariance matrices and the GMVP composition is computed afterwards. We show analytically that either direct or indirect ES predictors of GMVP proportions could be advantageous but neither of them dominates. For this reason we suggest a dynamic time series approach in order to combine them. We illustrate our findings in an empirical study for GMVPs based on 100 risky assets and report that the proposed ES forecast combination is suitable for GMVP prediction.

Keywords: Forecast combination; Minimum variance portfolio; Realized covariance matrix; Variance change (search for similar items in EconPapers)
JEL-codes: C32 C58 G11 G17 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:empfin:v:53:y:2019:i:c:p:222-237

DOI: 10.1016/j.jempfin.2019.07.006

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