Search Results - subject:"Markowitz portfolio selection"

Large dynamic covariance matrices: Enhancements based on intraday data

De Nard, Gianluca; Engle, Robert F.; Ledoit, Olivier; … - 2022

Multivariate GARCH models do not perform well in large dimensions due to the so-called curse of dimensionality. The recent DCC-NL model of Engle et al. (2019) is able to overcome this curse via nonlinear shrinkage estimation of the unconditional correlation matrix. In this paper, we show how...

Persistent link: https://www.econbiz.de/10013164130

Large dynamic covariance matrices: enhancements based on intraday data

De Nard, Gianluca; Engle, Robert F.; Ledoit, Olivier; … - 2022 - This version: January 2022

Persistent link: https://www.econbiz.de/10013040932

Large dynamic covariance matrices: Enhancements based on intraday data

De Nard, Gianluca; Engle, Robert F.; Ledoit, Olivier; … - 2021

Persistent link: https://www.econbiz.de/10012588495

Using, taming or avoiding the factor zoo? : a double-shrinkage estimator for covariance matrices

De Nard, Gianluca; Zhao, Zhao - In: Journal of empirical finance 72 (2023), pp. 23-35

Persistent link: https://www.econbiz.de/10014476795

Large dynamic covariance matrices: Enhancements based on intraday data

De Nard, Gianluca; Engle, Robert F.; Ledoit, Olivier; … - 2020

as Markowitz portfolio selection. A popular tool to this end are multivariate GARCH models. Historically, such models did …

Persistent link: https://www.econbiz.de/10012253774

The power of (non-)linear shrinking: A review and guide to covariance matrix estimation

Ledoit, Olivier; Wolf, Michael - 2020

portfolio selection. When the number of variables is of the same magnitude as the number of observations, this constitutes a …Many econometric and data-science applications require a reliable estimate of the covariance matrix, such as Markowitz …

Persistent link: https://www.econbiz.de/10012166460