A new formula for converting a covariance matrix estimated in local currencies into a covariance matrix expressed in a common currency is proposed. This process uses simple matrix multiplications. We ...

This paper proposes a novel shrinkage estimator for high-dimensional covariance matrices by extending the Oracle Approximating Shrinkage (OAS) of Chen et al. (2009) to target the diagonal elements of ...

We consider estimation of the covariance matrix of a multivariate random vector under the constraint that certain covariances are zero. We first present an algorithm, which we call iterative ...

The COV= option must be specified to compute an approximate covariance matrix for the parameter estimates under asymptotic theory for least-squares, maximum-likelihood, or Bayesian estimation, with or ...

A flexible class of prior distributions is proposed, for the covariance matrix of a multivariate normal distribution, yielding much more general hierarchical and empirical Bayes smoothing and ...

This study contributes to the ongoing discussion by investigating whether risk factor disclosures contain valuable information that can be used to improve the estimation of the covariance matrix of ...

Covariance matrix estimation, crucial for multivariate inference, faces significant challenges when the number of variables rivals or exceeds the sample size.

The estimated covariance matrix of the parameter estimates is computed as the inverse Hessian matrix, and for unconstrained problems it should be positive definite. If the final parameter estimates ...