bootstrap to achieve valid inference in a time series setting. The test statistics and the estimators are computed using linear …

An intensive and still growing body of research focuses on estimating a portfolio’s Value-at-Risk. Depending on both … distribution specifications or historical and Monte Carlo simulation methods. Although these approaches to overall VaR estimation …, component VaR and incremental VaR readily follow. The proposed estimation approach pairs intuitive appeal with computational …

An intensive and still growing body of research focuses on estimating a portfolio’s Value-at-Risk.Depending on both the … orhistorical and Monte Carlo simulation methods. Although these approaches to overall VaR estimation have receivedsubstantial … proposed estimation approach pairs intuitiveappeal with computational efficiency. We evaluate various alternative estimation …

This paper uses Monte Carlo simulation analysis to study the finite-sample behavior of bootstrap estimators and tests … tailored to handle heteroskedasticity. Our results show that weighted bootstrap methods can be successfully used to estimate … test and of Bartlett and Edgeworth-corrected tests. The bootstrap test was found to be robust against unfavorable …

This note shows that two ways of simulation based bias correction–indirect inference and bootstrap bias correction …

samples than the confidence regions that can be constructed from the smoothed bootstrap method recently suggested by Horowitz …

In this paper we consider kernel estimation of a density when the data are contaminated by random noise. More …

classical bootstrap and the robust bootstrap, in terms of accuracy and robustness. These results show that robustness is a key …

their quantile breakdown point. For the block bootstrap and the sub- sampling, we find a very low quantile breakdown point …, which can ren- der inferences based on standard resampling methods useless already in simple estimation and testing settings …