Abad Romero, PilarBenito Muela, SoniaSánchez Granero, Miguel AngelLópez, Carmen2023-06-192023-06-192013-12https://hdl.handle.net/20.500.14352/41535This work has been funded by the Spanish Ministerio de Ciencia y Tecnología (ECO2009-10398/ECON and ECO2011-23959).This paper evaluates the performance of several skewed and symmetric distributions in modeling the tail behavior of daily returns and forecasting Value at Risk (VaR). First, we used some goodness of fit tests to analyze which distribution best fits the data. The comparisons in terms of VaR have been carried out examining the accuracy of the VaR estimate and minimizing the loss function from the point of view of the regulator and the firm. The results show that the skewed distributions outperform the normal and Student-t (ST) distribution in fitting portfolio returns. Following a two-stage selection process, whereby we initially ensure that the distributions provide accurate VaR estimates and then, focusing on the firm´s loss function, we can conclude that skewed distributions outperform the normal and ST distribution in forecasting VaR. From the point of view of the regulator, the superiority of the skewed distributions related to ST is not so evident. As the firms are free to choose the VaR model they use to forecast VaR, in practice, skewed distributions will be more frequently used.engAtribución-NoComercial 3.0 Españahttps://creativecommons.org/licenses/by-nc/3.0/es/technical reporthttps://www.ucm.es/icaeopen accessValue at RiskParametric modelSkewness t-Generalised DistributionGARCH ModelRisk ManagementLoss function.Econometría (Economía)5302 Econometría