We propose a jump robust positive semidefinite rank-based estimator for the daily covariance matrix based on high-frequency intraday returns. It disentangles covariance estimation into variance and correlation components. This allows to estimate correlations over lower sampling frequencies, to...

The Gaussian rank correlation equals the usual correlation coefficient computed from the normal scores of the data. Although its influence function is unbounded, it still has attractive robustness properties. In particular, its breakdown point is above 12%. Moreover, the estimator is consistent...

Full paper is available at: "https://ssrn.com/abstract=3087336" https://ssrn.com/abstract=3087336.In this supplementary appendix to the paper Boudt, Cornilly and Verdonck (2019), we first provide a brief R tutorial for the proposed NC estimator. Then, we go into more detail about the shape of...

We propose a minimum distance estimator for the higher-order comoments of a multivariate distribution exhibiting a lower dimensional latent factor structure. We derive the influence function of the proposed estimator and prove its consistency and asymptotic normality. The simulation study...