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{\LARGE On the estimation of extreme values for risk assessment and management: The average conditional exceedance rate method}
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\section*{Abstract}
For prediction of tail quantiles we have used the average conditional
exceedance rate (ACER) method for prediction of extreme values,
generalized to be able to accommodate heavy tailed data. The ACER
method has been applied to both the logarithmic returns and to the
residuals after an AR-GARCH filtering of the returns for daily
electricity spot prices from Nord Pool. Since the data used are highly
seasonal and displays significant serial correlation and volatility
clustering, the conditional approach with the ACER method applied to
the residuals is of interest here. Earlier it has been observed that
the use of the peaks-over-threshold (POT) method, compared to just the
use of an AR-GARCH model, to predict the residual tail quantiles
produces more accurate estimates when the observed price changes are
extreme, as observed in the hourly spot price data from the Nordic
exchange for electricity trading, Nord Pool. Following this we have
applied the ACER method to the AR-GARCH filtered price changes in the Nord Pool data. 
These analyses have led to the observations that the ACER method is able to estimate the tail
quantiles even more accurately than the POT method for these data.
Where the POT method uses the generalized Pareto distribution (GPD) to
fit observations over a given threshold, the ACER method fit the
observations to an extreme value distribution modified to capture to
some extent also sub-asymptotic behavior. In principle, since there
is no asymptotic assumption involved in the derivation of this
distribution, the ACER method is able to utilize more of the
observations than the asymptotic alternatives, leading to more
accurate estimate of the tails. This is expected to be the case for
data where the appropriate choice of the threshold in the POT method
leads to a high proportion of discarded observations.
