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In this paper we focus on the impact of additive outliers (level and volatility) on the calculation of risk measures, such as minimum capital risk requirements. Through simulation and empirical studies, we compare six alternative proposals that are used in the literature to reduce the effects of outliers in the estimation of risk measures when using GARCH-type models. The methods are based on  correcting for significant outliers,  accommodating outliers using complex (e.g. fat-tail) distributions and  accounting for outlier effects by robust estimation. The main conclusions of the simulation study are that the presence of outliers bias these risk measures, being the proposal by Grane and Veiga (2010) that providing the highest bias reduction. From the out-of-sample results for four international stock market indexes we found weak evidence that more complex models (specification and error distribution) perform better in estimating the minimum capital risk requirements during the last global financial crisis. (C) 2014 Elsevier B.V. All rights reserved.
minimum capital risk requirements; outliers; robust estimation; wavelets