This paper proposes a novel regularisation method for the estimation of large covariance matrices, which makes use of insights from the multiple testing literature. The method tests the statistical significance of individual pair-wise correlations and sets to zero those elements that are not...

This paper proposes a regularisation method for the estimation of large covariance matrices that uses insights from the multiple testing (MT) literature. The approach tests the statistical significance of individual pair-wise correlations and sets to zero those elements that are not...

This paper proposes a novel regularisation method for the estimation of large covariance matrices, which makes use of insights from the multiple testing literature. The method tests the statistical significance of individual pair-wise correlations and sets to zero those elements that are not...

In this paper, we show that the order of magnitude of the finite sample bias of the GMMld^{(2)} estimator of Bun and Kiviet (2006) can be reduce from O(T/N) to O(1/N) if the optimal weighting matrix is used. To demonstrate this result, we consider a model transformed by the upper triangular...