A new approach to characterizing and forecasting electricity price volatility

Chan, K. F., Gray, P. and van Campen, B. (2008) A new approach to characterizing and forecasting electricity price volatility. International Journal of Forecasting, 24 4: 728-743. doi:10.1016/j.ijforecast.2008.08.002

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Author Chan, K. F.
Gray, P.
van Campen, B.
Title A new approach to characterizing and forecasting electricity price volatility
Journal name International Journal of Forecasting   Check publisher's open access policy
ISSN 0169-2070
Publication date 2008
Year available 2008
Sub-type Article (original research)
DOI 10.1016/j.ijforecast.2008.08.002
Volume 24
Issue 4
Start page 728
End page 743
Total pages 16
Editor R. J. Hyndman
Place of publication Amsterdam, The Netherlands
Publisher Elsevier BV
Collection year 2009
Language eng
Subject C1
150201 Finance
900101 Finance Services
Abstract There is a growing need to model the dynamics of electricity spot prices. While many studies have adopted the jump-diffusion model used successfully in traditional financial markets, the distinctive features of energy prices present non-trivial challenges. In particular, electricity price series feature extreme jumps of magnitudes rarely seen in financial markets, and occurring at greater frequency. Standard parametric approaches to estimating jump-diffusion models struggle to disentangle the jump and non-jump variation. This paper explores a recently-developed approach to separating the total variation into jump and non-jump components. Using quadratic variation theory, we non-parametrically estimate jump parameters for five power markets which are known to feature some important physical differences. The unique characteristics of the jump and non-jump components of the total variation are studied for each market. Given the evidence that the two sources of variation in spot prices have distinct dynamics, the paper explores whether volatility forecasts can be improved by explicitly incorporating the jump and non-jump components of the total variation.
Keyword Realized volatility
Bipower variation
Quadratic variation
Volatility forecast
Q-Index Code C1
Q-Index Status Confirmed Code

Document type: Journal Article
Sub-type: Article (original research)
Collections: 2009 Higher Education Research Data Collection
Excellence in Research Australia (ERA) - Collection
UQ Business School Publications
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Citation counts: TR Web of Science Citation Count  Cited 23 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 29 times in Scopus Article | Citations
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Created: Wed, 05 Nov 2008, 15:05:28 EST by Karen Morgan on behalf of UQ Business School