Forecasting spikes in electricity return innovations

Tafakori, L, Pourkhanali, A and Alavi Fard, F 2018, 'Forecasting spikes in electricity return innovations', Energy, vol. 150, pp. 508-526.

Document type: Journal Article
Collection: Journal Articles

Title Forecasting spikes in electricity return innovations
Author(s) Tafakori, L
Pourkhanali, A
Alavi Fard, F
Year 2018
Journal name Energy
Volume number 150
Start page 508
End page 526
Total pages 19
Publisher Elsevier
Abstract This paper evaluates the accuracy of several hundred one-day-ahead value at risk (VaR) forecasts for predicting Australian electricity returns. We propose a class of observation-driven time series models referred to as asymmetric exponential generalised autoregressive score (AEGAS) models. The mechanism to update the parameters over time is provided by the scaled score of the likelihood function in the AEGAS model. Based on this new approach, the results provide a unified and consistent framework for introducing time-varying parameters in a wide class of non-linear models. The Australian energy markets is known as one of the most volatile and, when compared to some well-known models in the recent literature as benchmarks the fitting and forecasting results demonstrate the superior performance and considerable flexibility of proposed model for electricity markets.
Subject Probability Theory
Statistical Theory
Applied Statistics
Keyword(s) Electricity spikes
AEGAS model
Volatility forecasting
DOI - identifier 10.1016/
Copyright notice © 2018 Elsevier Ltd
ISSN 0360-5442
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Citation counts: TR Web of Science Citation Count  Cited 4 times in Thomson Reuters Web of Science Article | Citations
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