  Using Monte Carlo simulation to calculate the Value at Risk (VaR) as a
possible risk measure requires adequate techniques. One of these techniques is
the application of a compound distribution for the aggregates in a portfolio.
In this paper, we consider the aggregated loss of Gamma distributed severities
and estimate the VaR by introducing a new approach to calculate the quantile
function of the Gamma distribution at high confidence levels. We then compare
the VaR obtained from the aggregation process with the VaR obtained from a
single loss distribution where the severities are drawn first from an
exponential and then from a truncated exponential distribution. We observe that
the truncated exponential distribution as a model for the severities yields
results closer to those obtained from the aggregation process. The deviations
depend strongly on the number of obligors in the portfolio, but also on the
amount of gross loss which truncates the exponential distribution.
