Abstract: In this paper we focus on the impact of additive level outliers on the calculation of risk measures, such as minimum capital risk requirements, and compare four alternatives of reducing the estimation biases of these measures. The first three proposals proceed by the detection and correction of outliers before estimating these risk measures with the GARCH(1,1) model, while the fourth procedure fits a Student’s t-distributed GARCH(1,1) model directly to the data. The former group includes the proposal by Grane and Veiga (2010), a detection procedure based on wavelets with hard- or soft-thresholding filtering, and the well known method by Franses and Ghijsels (1999). The first results, based on Monte Carlo experiments, reveal that the presence of outliers can bias severely the minimum capital risk requirement estimates calculated using the GARCH(1,1) model. The message derived from the second results, both empirical and simulations, is that outlier detection and filtering generate more accurate minimum capital risk re- quirements than the fourth alternative. Moreover, the detection procedure based on wavelets with hard-thresholding provides a very good performance for attenuating the effects of outliers and generating accurate minimum capital risk requirements out-of-sample, even in quite volatile periods.