ABSTRACT
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 chapter 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 dependence. We suggest using stable Paretian distributions in VAR modeling.
