We introduce a regularization and blocking estimator for well-conditioned high-dimensional daily covariances using high-frequency data. Using the Barndorff-Nielsen, Hansen, Lunde, and Shephard (2008a) kernel estimator, we estimate the covariance matrix block-wise and regularize it. A data-driven...

In this paper, a method for estimating monotone, convex and log-concave densities is proposed. The estimation procedure consists of an unconstrained kernel estimator which is modi?ed in a second step with respect to the desired shape constraint by using monotone rearrangements. It is shown that...

Proofs for the consistency of the kernel density estimator have historically developed. Four important milestones are the pointwise consistency, the almost sure uniform convergence, the rate of convergence on a bounded interval and the rate of convergence on R. The underlying concepts of total...