  Spectral risk measures are attractive risk measures as they allow the user to
obtain risk measures that reflect their subjective risk-aversion. This paper
examines spectral risk measures based on an exponential utility function, and
finds that these risk measures have nice intuitive properties. It also
discusses how they can be estimated using numerical quadrature methods, and how
confidence intervals for them can be estimated using a parametric bootstrap.
Illustrative results suggest that estimated exponential spectral risk measures
obtained using such methods are quite precise in the presence of normally
distributed losses.
