Optimization of Monte Carlo Procedures for Value at Risk Estimates

type="main" xml:lang="en"> <p>This paper proposes a methodology which improves the computational efficiency of the Monte Carlo simulation approach of value at risk (VaR) estimates. Principal components analysis is used to reduce the number of relevant sources of risk driving the portfolio dynamics. Moreover, large deviations techniques are used to provide an estimate of the minimum number of price scenarios to be simulated to attain a given accuracy. Numerical examples are provided and show the good performance of the methodolgy proposed. <p>(J.E.L.: C15, G1).