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• W2121209256 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." @default.
• W2121209256 created "2016-06-24" @default.
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• W2121209256 date "2007-03-01" @default.
• W2121209256 modified "2023-09-23" @default.
• W2121209256 title "Heavy tails and electricity prices: Do time series models with non-Gaussian noise forecast better than their Gaussian counterparts?" @default.
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