Heavy tails and electricity prices: Do time series models with non-Gaussian noise forecast better than their Gaussian counterparts?

MPRA Paper from University Library of Munich, Germany

Abstract: This paper is a continuation of our earlier studies on short-term price forecasting of California electricity prices with time series models. Here we focus on whether models with heavy-tailed innovations perform better in terms of forecasting accuracy than their Gaussian counterparts. Consequently, we limit the range of analyzed models to autoregressive time series approaches that have been found to perform well for pre-crash California power market data. We expand them by allowing for heavy-tailed innovations in the form of α-stable or generalized hyperbolic noise.

Keywords: Electricity; price forecasting; heavy tails; time series; α-stable distribution; generalized hyperbolic distribution (search for similar items in EconPapers)
JEL-codes: C22 C46 C53 Q40 (search for similar items in EconPapers)
Date: 2007-03, Revised 2007-10
New Economics Papers: this item is included in nep-ecm, nep-ene, nep-ets and nep-for
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:2292

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