 Electricity prices forecast analysis using the extreme value theoryMario Domingues de Paula Simões, Marcelo Klotzle, Antonio Carlos Figueiredo Pinto and Leonardo Lima Gomes International Journal of Financial Markets and Derivatives, 2016, vol. 5, issue 1, 1-22 Abstract: The present work attempts to evaluate the risk attached to electricity price forecasts. Initially, an analysis of prices series from different observation frequencies and, as expected, the volatility attenuation as a function of decreased observation frequency, for the same data, is observed. Next, a price forecast is made using a widely established and well used ARMA model. The distribution of residues of this forecast is modelled by a Gaussian curve and a generalised Pareto distribution, as well as its empirical distribution, following which the risk metrics VaR and CVaR are calculated. The Gaussian approximation shows to be appropriate for the estimation of forecast errors at low quantiles, up to 95%, for both daily and hourly data, but underestimates CVaR. The GPD distribution proves to be accurate and safe for the use of CVaR at any observation frequency, while it is introduced a novel GPD combination technique for the use of CVaR at extreme quantiles. Keywords: electricity prices; risk assessment; VaR; CVaR; EVT; GPD; generalised Pareto distribution; out-of-sample forecasting; price forecasting; extreme value theory; ARMA model; modelling. (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:ids:ijfmkd:v:5:y:2016:i:1:p:1-22 Access Statistics for this article More articles in International Journal of Financial Markets and Derivatives from Inderscience Enterprises Ltd |
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