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dc.contributor.authorDahlen, Kai Erik
dc.contributor.authorSolibakke, Per Bjarte
dc.contributor.authorWestgaard, Sjur
dc.contributor.authorNæss, Arvid
dc.date.accessioned2020-03-24T09:19:53Z
dc.date.available2020-03-24T09:19:53Z
dc.date.created2014-08-08T12:00:30Z
dc.date.issued2015
dc.identifier.citationInternational journal of business. 2015, 20 (1), 33-51.
dc.identifier.issn1083-4346
dc.identifier.urihttps://hdl.handle.net/11250/2648271
dc.description.abstractIn 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.
dc.language.isoeng
dc.relation.urihttp://www.craig.csufresno.edu/ijb/Volumes/Volume%2020/V201-2.pdf
dc.titleOn the Estimation of Extreme Values for Risk Assessment and Management: The ACER Method
dc.typePeer reviewed
dc.typeJournal article
dc.description.versionpublishedVersion
dc.source.pagenumber33-51
dc.source.volume20
dc.source.journalInternational journal of business
dc.source.issue1
dc.identifier.cristin1145840
dc.relation.projectNorges forskningsråd: 228811
cristin.unitcode211,5,0,0
cristin.unitnameAvdeling for økonomi og samfunnsvitenskap
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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