Evaluating the performance of the skewed distributions to forecast value-at-risk in the global financial crisis

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.