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A new approach to characterizing and forecasting electricity price volatility

  • Chan, Kam Fong
  • Gray, Philip
  • van Campen, Bart
Registered author(s):

    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.

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    Article provided by Elsevier in its journal International Journal of Forecasting.

    Volume (Year): 24 (2008)
    Issue (Month): 4 ()
    Pages: 728-743

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    Handle: RePEc:eee:intfor:v:24:y:2008:i:4:p:728-743
    Contact details of provider: Web page: http://www.elsevier.com/locate/ijforecast

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    1. Soares, Lacir Jorge & Souza, Leonardo Rocha, 2006. "Forecasting electricity demand using generalized long memory," International Journal of Forecasting, Elsevier, vol. 22(1), pages 17-28.
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