eigenvalues of T converges weakly to a probability distribution. We consider the renormalized sample covariance matrix H̃=np(1nT1 …

estimate the covariance matrix of the latent states through a Kalman smoother and Expectation Maximization (KEM) algorithm. In …. Extensive Monte Carlo simulations show the superior performance of the KEM estimator over several alternative covariance matrix … likelihood obtained with the reconstructed price series. Iterating between the two EM steps, we obtain a KEM-improved covariance …

The estimation of the inverse covariance matrix plays a crucial role in optimal portfolio choice. We propose a new … shrinkage directly to the inverse covariance matrix using two non-parametric methods. The first minimises the out …

Selecting an estimator for the covariance matrix of a regression's parameter estimates is an important step in … robust covariance matrix estimator is that tests conducted using this estimator can have better power properties. This … motivates tests that examine the appropriate level of robustness in covariance matrix estimation. In this paper, we propose a …

of the reasons for the bias lies in an ill-conditioned sample covariance matrix, which is used in the so-called variance … shrinkage to target methods for the sample covariance matrix. Alternatively, the identity matrix, a single factor model, and …

heteroskedasticity consistent covariance matrix estimators is also considered. It is found that these 'robust' methods are unable to …