Predicting volatility is of great importance in measuring and managing risk more accurately. Volatility is measured, estimated and predicted by a vast number of model frameworks. Though, researcher has to select a specific model for his/her forecasting purposes. Thus, the models are evaluated in order to extract the most adequate model for forecasting purposes.Under the Marie Curie Intra-European Fellowships Grand, we define a method of evaluating the predictability of the models, which assumes that the distance between the actual volatility and its forecast is normally distributed. However, empirical applications provide evidence that the distribution of this distance is better described via a leptokurtotic and asymmetric distribution. Under this grand we intend to construct a model evaluation method that presumes a leptokurtic and asymmetric distributed distance between the actual volatility and its forecast. The development of a volatility forecasting evaluation framework, with assumptions closer to reality, is of great importance in producing accurate forecasts of risk measures (specially the last years with the deep crisis caused in financial sectors).