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Shrinkage estimation of large covariance matrices : keep it simple, statistician?

Ledoit, Olivier; Wolf, Michael - 2020 - This version: February 2020

Under rotation-equivariant decision theory, sample covariance matrix eigenvalues can be optimally shrunk by recombining sample eigenvectors with a (potentially nonlinear) function of the unobservable population covariance matrix. The optimal shape of this function reflects the loss/risk that is...

Persistent link: https://www.econbiz.de/10012165715

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

Ledoit, Olivier; Wolf, Michael - 2020 - This version: February 2020

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/10012165719

Estimation of time-varying covariance matrices for large datasets

Dendramis, Yiannis; Giraitis, Liudas; Kapetanios, George - 2020

Unemployment, firm Dynamics, and the Business CyclTime variation is a fundamental problem in statistical and econometric analysis of macroeconomic and financial data. Recently there has been considerable focus on developing econometric modelling that enables stochastic structural change in model...

Persistent link: https://www.econbiz.de/10012316010

Estimation of Large Correlation Matrix with Shrinking Methods

Posch, Peter N. - 2016

An exact estimation of the true correlation matrix is highly desirable in many applications. In practice there will … correlation matrix can be calculated when there are fewer observations than assets. We compare several shrinking methods regarding … their correlation matrix estimation using several data generating processes. We calculate the distance of the estimator to …

Persistent link: https://www.econbiz.de/10012996606

A New Semiparametric Estimation Approach of Large Dynamic Covariance Matrices with Multiple Conditioning Variables

Chen, Jia - 2018

This paper studies the estimation of dynamic covariance matrices with multiple conditioning variables, where the matrix size can be ultra large (divergent at an exponential rate of the sample size). We introduce an easy-to-implement semiparametric method to estimate each entry of the covariance...

Persistent link: https://www.econbiz.de/10012915138

Shrinkage Estimation of Large Covariance Matrices : Keep it Simple, Statistician?

Ledoit, Olivier - 2020

Persistent link: https://www.econbiz.de/10012848575

Large Covariance Estimation by Bootstrapped Hierarchies and Shrinkage

Bongiorno, Christian; Tan, Vincent; Zohren, Stefan - 2022

This paper introduces a large-dimensional covariance estimator that exploits the hierarchical structure in financial returns. Prevailing techniques that filter the noise in a covariance matrix according to hierarchical agglomeration are fragile to data perturbations and inordinately suppress...

Persistent link: https://www.econbiz.de/10014239116

The Asymptotics for Panel Models with Common Shocks

Kao, Chihwa - 2011

This paper considers models with latent/discrete endogenous regressors and presents a simulation-based two-step (STS) estimator. The endogeneity is corrected by adopting a simulation-based control function approach. The first step consists of simulating of the residuals of the reduced-form...

Persistent link: https://www.econbiz.de/10013126681

Markowitz portfolios under transaction costs

Ledoit, Olivier; Wolf, Michael - 2022

Markowitz portfolio selection is a cornerstone in finance, both in academia and in the industry. Most academic studies either ignore transaction costs or account for them in a way that is both unrealistic and suboptimal by (i) assuming transaction costs to be constant across stocks and (ii)...

Persistent link: https://www.econbiz.de/10013440073

On the impact of serial dependence on penalized regression methods

Tonini, Simone; Chiaromonte, Francesca; Giovannelli, … - 2022

regressions (PRs). Focusing on the direct relationship between the degree of cross-correlation of covariates and the estimation …

Persistent link: https://www.econbiz.de/10013336165