This paper proposes a regularisation method for the estimation of large covariance matrices that uses insights from the … of estimating the theoretical constant arising in the rate of convergence of existing thresholding estimators, and hence … it is easy to implement and does not require cross-validation. The MT estimator of the sample correlation matrix is shown …

multiple testing (MT) estimator to a number of thresholding and shrinkage estimators in the literature in a detailed Monte …This paper proposes a novel regularisation method for the estimation of large covariance matrices, which makes use of … theoretical constant arising in the rate of convergence of existing thresholding estimators. We compare the performance of our …

multiple testing (MT) estimator to a number of thresholding and shrinkage estimators in the literature in a detailed Monte …This paper proposes a novel regularisation method for the estimation of large covariance matrices, which makes use of … theoretical constant arising in the rate of convergence of existing thresholding estimators. We compare the performance of our …

modelling that enables stochastic structural change in model parameters and on model estimation by Bayesian or non …-parametric kernel methods. In the context of the estimation of covariance matrices of large dimensional panels, such data requires … applicable in econometric analysis beyond estimation of large covariance matrices. We discuss the utility of the robust …

Existing shrinkage techniques struggle to model the covariance matrix of asset returns in the presence of multiple …-asset classes. Therefore, we introduce a Blockbuster shrinkage estimator that clusters the covariance matrix accordingly. Besides … the definition and derivation of a new asymptotically optimal linear shrinkage estimator we propose an adaptive …

competing estimators. An out-of-sample GMV portfolio allocation problem is studied. A simple shrinkage technique is introduced …