In this paper we analyze the time series of daily average prices generated in the Italian electricity market, which started to operate as a Pool in April 2004. The objective is to characterize the high degree of autocorrelation and multiple seasonalities in the electricity prices. We use periodic time series models with GARCH disturbances and leptokurtic distributions and compare their performance with more classical ARMA-GARCH processes. The within-year seasonal variation is modelled using the low frequencies components of physical quantities, which are very regular throughout the sample. Results reveal that much of the variability of the price series is explained by deterministic multiple seasonalities which interact with each other. Periodic AR-GARCH models seem to perform quite well in mimicking the features of the stochastic part of the price process.
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Paper provided by Università degli Studi di Milano-Bicocca, Dipartimento di Statistica in its series Working Papers with number
20060301.