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Engle, Robert F.; Ledoit, Olivier; Wolf, Michael - 2017

for conditional heteroskedasticity; a favored model is Dynamic Conditional Correlation (DCC), derived from the ARCH/GARCH …

Persistent link: https://www.econbiz.de/10011663190

Beyond sorting: A more powerful test for cross-sectional anomalies

Ledoit, Olivier; Wolf, Michael; Zhao, Zhao - 2016

Many researchers seek factors that predict the cross-section of stock returns. The standard methodology sorts stocks according to their factor scores into quantiles and forms a corresponding long-short portfolio. Such a course of action ignores any information on the covariance matrix of stock...

Persistent link: https://www.econbiz.de/10011663197

Multivariate Skewed Student's t Copula in the Analysis of Nonlinear and Asymmetric Dependence in the German Equity Market

Sun, Wei; Rachev, Svetlozar; Stoyanov, Stoyan; Fabozzi, … - In: Studies in Nonlinear Dynamics & Econometrics 12 (2008) 2, pp. 1572-1572

several advantages compared to the linear correlation measure in modeling comovement. This paper introduces a copula ARMA-GARCH … model for analyzing the comovement of indexes in German equity markets. The model is implemented with an ARMA-GARCH model … skewed Student's t copula ARMA(1,1)-GARCH(1,1) model with Lévy fractional stable noise is superior to alternative models …

Persistent link: https://www.econbiz.de/10005046500

Factor models for portfolio selection in large dimensions: The good, the better and the ugly

De Nard, Gianluca; Ledoit, Olivier; Wolf, Michael - 2018

This paper injects factor structure into the estimation of time-varying, large-dimensional covariance matrices of stock returns. Existing factor models struggle to model the covariance matrix of residuals in the presence of conditional heteroskedasticity in large universes. Conversely,...

Persistent link: https://www.econbiz.de/10011969201

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

Ledoit, Olivier; Wolf, Michael - 2019

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

Persistent link: https://www.econbiz.de/10012026512

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

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 …

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

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

Ledoit, Olivier; Wolf, Michael - 2020

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

Large dynamic covariance matrices: Enhancements based on intraday data

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

Multivariate GARCH models do not perform well in large dimensions due to the so-called curse of dimensionality. The …

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

VaR Forecast and Dynamic Conditional Correlations for Spot and Futures Returns on Stocks and Bonds

McAleer, Michael; Hakim, Hakim, A. - Faculteit der Economische Wetenschappen, Erasmus … - 2009

The paper investigates the interdependence and conditional correlations between futures contracts and their underlying assets, both for stock and bond markets, and the impact of the interdependence and conditional correlations on VaR forecasts. The paper finds evidence of volatility spillovers...

Persistent link: https://www.econbiz.de/10010731676