Value at Risk: Recent Advances.

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 review the recent advances in the VAR methodologies The proposed improvements still lack a convincing unified technique capturing the observed phenomena in financial data such as heavy-tails, time-varying volatility, and short- and long-range depedence. We suggest to use stable Paretian distributions in VAR modeling.