  Spectral risk measures (SRMs) belong to the family of coherent risk measures.
A natural estimator for the class of SRMs has the form of L-statistics. Various
authors have studied and derived the asymptotic properties of the empirical
estimator of SRM. We propose a kernel based estimator of SRM. We investigate
the large sample properties of general L-statistics based on i.i.d and
dependent observations and apply them to our estimator. We prove that it is
strongly consistent and asymptotically normal. We compare the finite sample
performance of our proposed kernel estimator with that of several existing
estimators for different SRMs using Monte Carlo simulation. We observe that our
proposed kernel estimator outperforms all the estimators. Based on our
simulation study we have estimated the exponential SRM of four future
indices-that is Nikkei 225, Dax, FTSE 100, and Hang Seng. We also discuss the
use of SRM in setting initial margin requirements of clearinghouses. Finally we
perform a backtesting exercise of SRM.
