Classical estimation techniques for linear models either are inconsistent, or perform somewhat poorly under stable error densities; most of them are not even rate-optimal. In this paper, we develop an original R-estimation method and investigate its asymptotic performances under stable...

Linear models with stable error densities are considered. The local asymptotic normality of the resulting model is established. We use this result, combined with Le~Cam's third lemma, to obtain local powers of various classical rank tests (Wilcoxon's and van der Waerden's test, the median test,...

We introduce an approximate dynamic factor model for modeling and forecasting large panels of realized volatilities. Since the model is estimated by means of principal components and low dimensional maximum likelihood, it does not suffer from the curse of dimensionality. We apply the model to a...

We propose two classes of semi-parametric estimators for the tail index of a regular varying elliptical random vector. The first one is based on the distance between a tail probability contour and the observations outside this contour. We denote it as the class of separating estimators. The...