propose a model-based bias-corrected estimation approach. Our simulation results indicate that bias strongly relates to the … substantial differences in measured tail-thickness due to small sample bias. As a consequence, high quantile estimation may lead …

propose a model-based bias-corrected estimation approach. Our simulation results indicate that bias strongly relates to the … substantial differences in measured tail-thickness due to small sample bias. As a consequence, high quantile estimation may lead …

A resampling method based on the bootstrap and a bias-correction step is developed for improving the Value-at-Risk (VaR …, easy to implement, numerically reliable, and, except for having to choose a window length L for the bias-correction step …

fluctuations not forecasted by Gaussian models. This paper applies a resampling method based on the bootstrap and a bias … long and short positions. Our aim is to utilize the advantages of this model, but still use the bootstrap resampling method … to accurate for the tendency of the model tomiscalculate the VaR. Empirical results indicate that the bias …

The main contribution of this paper is a proof of the asymptotic validity of the application of the bootstrap to AR … establishing that a suitably constructed bootstrap estimator will have the same limit distribution as the least-squares estimator … robust standard errors or the bootstrap approximation of the distribution of autoregressive parameters. A simulation study …

This paper proposes a bootstrap unit root test in models with GARCH(1,1) errors and establishes its asymptotic validity … under mild moment and distributional restrictions. While the proposed bootstrap test for a unit root shares the power … particular, the bootstrap procedure does not require explicit estimation of nuisance parameters that enter the distribution of …