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A generalized exponential time series regression model for electricity prices

Publikation: Working paper/Preprint › Working paper › Forskning

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  • rp16_08

    Forlagets udgivne version, 1,28 MB, PDF-dokument

  • Niels Haldrup
  • Oskar Knapik
    • ,
  • Tomasso Proietti, University of Rome “Tor Vergata” and Creates, Italien
We consider the issue of modeling and forecasting daily electricity spot prices on the Nord Pool Elspot power market. We propose a method that can handle seasonal and non-seasonal persistence by modelling the price series as a generalized exponential process. As the presence of spikes can distort the estimation of the dynamic structure of the series we consider an iterative estimation strategy which, conditional on a set of parameter estimates, clears the spikes using a data cleaning algorithm, and reestimates the parameters using the cleaned data so as to robustify the estimates. Conditional on the estimated model, the best linear predictor is constructed. Our modeling approach provides good fit within sample and outperforms competing benchmark predictors in terms of forecasting accuracy. We also find that building separate models for each hour of the day and averaging the forecasts is a better strategy than forecasting the daily average directly.
OriginalsprogEngelsk
UdgivelsesstedAarhus
UdgiverInstitut for Økonomi, Aarhus Universitet
Antal sider24
StatusUdgivet - 21 mar. 2016
SerietitelCREATES Research Paper
Nummer2016-08

    Forskningsområder

  • Robust estimation, long-memory, seasonality, electricity spot prices, Nord Pool power market, forecasting, robust Kalman lter, generalized exponential model

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