We cionsider semiparmetric assymetric kernel density estimators when the unkonwn density has support on [0,∞). We provide a unifying framework which contains assymmetric kernel versions of several semiparametric density estimators considered previously in the literature. This framework allows...

In this paper, we characterize explicitly the first derivative of the Value at Risk and the Expected Shortfall with respect to portfolio allocation when netting between positions exists. As a particular case, we examine a simple Gaussian example in order to illustrate the impact of netting...

We propose a general robust semiparametric bootstrap method to estimate conditional predictive distributions of GARCH-type models. Our approach is based on a robust estimator for the parameters in GARCH-type models and a robustified resampling method for standardized GARCH residuals, which...

We introduce a new approach on shape preserving estimation of cumulative distribution functions and probability density functions using the wavelet methodology for multivariate de- pendent data. Our estimators preserve shape constraints such as monotonicity, positivity and integration to one,...