On the estimation of extreme values for risk assessment and management: the ACER method

In this paper we use an Average Conditional Exceedance Rate (ACER) method to model the tail of the price change distribution of daily spot prices in the Nordic electricity market, Nord Pool Spot. We use an AR-GARCH model to remove any seasonality, serial correlation and heteroskedasticity from the d...

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Bibliographic Details
Published in:International journal of business 2015, Vol.20 (1), p.33
Main Authors: Dahlen, Kai Erik, Solibakke, Per Bjarte, Westgaard, Sjur, Naess, Arvid
Format: Article
Language:English
Subjects:
Commodity markets
Deregulation
Electricity generation
Forecasting
Hydroelectric power
Parameter estimation
Pareto optimum
Power marketers
Price level changes
Prices
Risk assessment
Seasonal variations
Spot market
Stochastic models
Studies
Volatility
Wind power
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Summary:In this paper we use an Average Conditional Exceedance Rate (ACER) method to model the tail of the price change distribution of daily spot prices in the Nordic electricity market, Nord Pool Spot. We use an AR-GARCH model to remove any seasonality, serial correlation and heteroskedasticity from the data before modelling the residuals from this filtering process with the ACER method. We show that using the conditional ACER method for Value-at-Risk forecasts give significant improvement over a standard AR-GARCH model with normal or Student's-t distributed errors. Compared to a conditional generalized Pareto distribution (GPD) fitted with the Peaks-over-Threshold (POT) method, the conditional ACER method produces slightly more accurate quantile forecasts for the highest quantiles.
ISSN:1083-4346