Electricity prices forecast analysis using the extreme value theory Online publication date: Thu, 16-Jun-2016
by Mario Domingues de Paula Simões; Marcelo Cabus Klotzle; Antonio Carlos Figueiredo Pinto; Leonardo Lima Gomes
International Journal of Financial Markets and Derivatives (IJFMD), Vol. 5, No. 1, 2016
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.
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