Extreme behaviour for bivariate elliptical distributions

The authors examine the asymptotic behaviour of conditional threshold exceedance probabilities for an elliptically distributed pair (X, Y) of random variables. More precisely, they investigate the limiting behaviour of the conditional distribution of Y given that X becomes extreme. They show that this behaviour differs between regularly and rapidly varying tails.     

Do political connections affect bank leverage? Evidence from some Middle Eastern and North African countries

This study examines the association between political patronage and bank financing decisions in a sample of 34 commercial banks operating in the Middle East and North Africa for the period 2003–2014. We collected information manually about the profiles and biographies of individuals on the boards of banks to identify which banks had political connections, which is the novel contribution of the study. Linear and nonlinear panel data analysis was used to investigate this relationship. The results reveal that politically backed banks tend to be more leveraged. The indirect effect of political patronage on leverage was not found to be large, but was significant through its interaction with profitability; that is, politically backed banks with higher profitability are positively associated with leverage. Our findings imply that the privileges resulting from political ties make banks more profitable, and that this also leads to higher leverage. In line with the related literature, we found that a strong political presence on the boards of banks can be considered an important intangible asset, and one of the factors driving bank financing decisions.

     

Computationally efficient classes of higher-order kernel functions

Classes of higher-order kernels for estimation of a probability density are constructed by iterating the twicing procedure. Given a kernel K of order l, we build a family of kernels K_m of orders l(m + 1) with the attractive property that their Fourier transforms are simply 1 — {1 —$(.)}^m+1, where ? is the Fourier transform of K. These families of higher-order kernels are well suited when the fast Fourier transform is used to speed up the calculation of the kernel estimate or the least-squares cross-validation procedure for selection of the window width. We also compare the theoretical performance of the optimal polynomial-based kernels with that of the iterative twicing kernels constructed from some popular second-order kernels.     

Note on the minimum mean integrated squared error of kernel estimates of a distribution function and its derivatives

An exact expiession for the minimum integrated squared error associated with the kernel distribution function and its derivatives is given. Furthermore, the virtual optimality of the Fourier integral estimate in density estimation, shown by Davis (1977), is extended to estimation of a distibution function and its derivatives.     

Local linear double and asymmetric kernel estimation of conditional quantiles

Abstract

In this work, we propose and investigate a family of non parametric quantile regression estimates. The proposed estimates combine local linear fitting and double kernel approaches. More precisely, we use a Beta kernel when covariate’s support is compact and Gamma kernel for left-bounded supports. Finite sample properties together with asymptotic behavior of the proposed estimators are presented. It is also shown that these estimates enjoy the property of having finite variance and resistance to sparse design.     

Nonparametric weighted symmetry tests

Weighted symmetry is an extension of the classical notion of symmetry in which the tails of a distribution are similar, up to a scaling factor. The authors develop test statistics of weighted symmetry based on empirical processes. The finite‐dimensional distributions of the proposed statistics are either non‐parametric or conditionally nonparametric, according as the parameters of weighted symmetry are known or estimated. Asymptotically, the distributions of the processes behave like Brownian bridges or motions, leading to familiar distributions for the proposed test statistics. The authors also establish the asymptotic normality of Hodges‐Lehmann type estimators based on a generalization of the Wilcoxon signed rank test. Furthermore, they propose density estimators in mat setting.     

Correction locale de l'estimateur à noyau de la densité d'une loi de probabilité

The standard Parzen-Rosenblatt kernel density estimator is known to systematically deviate from the true value near critical points of the density curve. To overcome this difficulty, we extend the Rao-Blackwell method by using locally sufficient statistics: we define a new estimator and study its asymptotic behaviour. The interest of the method is shown by means of simulations.