Value at Risk Estimation for Non-Gaussian Distributions

This paper presents a methodology for computing Value at Risk for Önancial as- sets that does not follow a normal distribution of return. A back-testing approach have been applied in order to select the best theoretical non-Gaussian distributions that can explain the behavior of the empirical data. In this study, Cauchy, Laplace, Logistic and Beta dis- tributions have been considered. As benchmark, historical distribution, and Extreme Value Theory (EVT) method have been used. The experiment suggests di§erences in estimation of over 5 times between one method and another.