Download PDF More Formats on IMF eLibrary Order a Print Copy Create Citation This paper proposes a novel shrinkage estimator for high-dimensional covariance matrices by extending the Oracle ...

A Bayesian method is proposed for estimating an inverse covariance matrix from Gaussian data. The method is based on a prior that allows the off-diagonal elements of the inverse covariance matrix to ...

The estimation of portfolio value-at-risk (VaR) requires a good estimate of the covariance matrix. As it is well known that a sample covariance matrix based on some historical rolling window is noisy ...

Primary Algorithm : Algorithmically, Sparse-Sparse multiplication problems manifests itself in three possible forms:(a) Multiplication of a sparse matrix with a sparse diagonal, sparse block-diagonal, ...

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 ...

This short paper demonstrates how a covariance matrix estimated using log returns of multiple assets in their respective base currencies can be converted directly into a covariance matrix in a single ...

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 ...