Sílvia Gonçalves

TL;DR: The asymptotic validity of three easy-to-implement alternative bootstrap proposals for stationary autoregressive processes with m.i.s.d. errors is established, finding them to be more accurate in small samples than the conventional large-sample approximation based on robust standard errors.

TL;DR: In this paper, the authors consider the independent and identically distributed (i.i.d.) bootstrap and the wild bootstrap (WB), and prove their first-order asymptotic validity under general assumptions on the log-price process that allow for drift and leverage effects.

TL;DR: In this article, the authors assess the precision of common dynamic models and quantify the magnitude of the estimation error by constructing confidence intervals around the point VAR and expected shortfall (ES) forecasts, and suggest a resampling technique that takes into account parameter estimation error in dynamic models of portfolio variance.

TL;DR: In this article, the authors provide a unified framework for analyzing bootstrapped extremum estimators of nonlinear dynamic models for heterogeneous dependent stochastic processes and prove the first-order asymptotic validity of the bootstrap distribution of suitable bootstrap analogs of Wald and Lagrange Multiplier statistics for testing hypotheses.

TL;DR: In this paper, conditions for the consistency of the moving blocks bootstrap estimators of the variance of the least square estimator in linear dynamic models with dependent data were established, and the use of bootstrap standard error estimates for bootstrapping Studentized statistics was discussed.