We suggest a method for estimating a covariance matrix on the basis of a sample of vectors drawn from a multivariate normal distribution. In particular, we penalize the likelihood with a lasso penalty ...

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 ...

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 ...

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 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 ...