Stable modeling of value at risk

The value-at-risk (VAR) measurements are widely applied to estimate exposure to market risks. The traditional approaches to VAR computations—the variance-covariance method, historical simulation, Monte Carlo simulation, and stress-testing—do not provide satisfactory evaluation of possible losses. In this paper, we analyze the use of stable Paretian distributions in VAR modeling.