For minimization, the inversion of the matrices in these formulas is done so that negative eigenvalues are considered zero, resulting always in a positive semidefinite covariance matrix. In small ...

We consider the problem of finding a valid covariance matrix in the foreign exchange market given an initial nonpositively semidefinite (non-PSD) estimate of such a matrix. The common no-arbitrage ...

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 ...

Asymptotic expansions of the non-null distributions of the likelihood ratio criteria for testing the equality of a covariance matrix, equality of a mean vector and a covariance matrix, independence ...

The formula is motivated by some recent and some old developments in random matrix theory and a requirement that it be explicitly invariant under a change of basis of risk factors. It may naturally be ...

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 ...