\section{Introduction}

In 1991 the Norwegian government decided that the power market should be deregulated allowing for power trading. Following this, the Nord Pool\index{Nord Pool market} market was established in 1996 as a common electricity market\index{electricity market} for Norway and Sweden. Finland followed into the Nord Pool market area in 1998, and western and eastern Denmark joined in 1999 and 2000 respectively. In 2002 Nord Pool spot\index{Nord Pool spot} was established and today runs the spot (1 day a-head) market for electricity in Norway, Sweden, Denmark, Finland and Estonia. Today, a large part of the consumption of electricity in this market is traded trough Nord Pool spot, and the spot market is the most important.

Due to the difficulties and costs of storing electricity (basically it cannot be stored) the observed spot price is highly volatile and the changes of these spot prices are often very large. Some stylized facts for the price changes in these spot data are that they display very heavy tails\index{heavy tails}, significant serial correlation\index{serial correlation}, seasonality\index{seasonality} and volatility clustering\index{volatility clustering} \citep{wero06}. The seasonality comes from the fact that the electricity cannot be stored, so the price during the high demand periods (during the day, weekdays and the winter) tend to be higher. Most of the electricity in the market is produced by hydro power plants, so prices are also highly influenced by precipitation, giving higher electricity prices in dry years. Extreme price changes\index{extreme price changes} or spikes are observed in electricity markets around the world \citep{Escribano}, and has been studied extensively over the last years. Earlier work on modelling these spikes as an error process \citep{Contreras,Garcia,Swider} applying extreme value theory, will be used here \citep{BYSTROM,FongChan2006283}. This chapter largely follows the work reported by \cite{dasowena15ijb}. 

In the examples presented, a conditional extreme value approach will be used, as suggested by \cite{McNeil2000271}, to estimate tail quantiles for the return distribution, and thus get a measure of the Value-at-Risk (VaR). They suggested that a Generalized Pareto (GP) distribution is to be fitted, with the use of the POT method, to a dataset of residuals from an AR-GARCH \citep{BROCKWELL} filtering process. Here, the ACER method will also be used to estimate the tails of these residuals, and the obtained results will be compared to those obtained with the use of the POT method. The methods will be compared in both in sample and out of sample performance. A reason for using the ACER method over the POT method, is that there are essentially no asymptotic arguments and no requirements about independent data in the derivation of the ACER method, which in turn leads to better use of the data. 







