Confidence intervals for multivariate value at risk

Confidence intervals for the γ-quantile of a linear combination of N non-normal variates with a linear dependence structure would be useful to the financial institutions as the intervals enable the accuracy of the value at risk (VaR) of a portfolio of investments to be quantified.Here we construct 100(1 -α)% confidence intervals for the γ-quantile using procedures based on bootstrap, normal approximation and hypothesis testing.We show that the method based on hypothesis testing produces a confidence interval which is more satisfactory than those found by using bootstrap or normal approximation.