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Risk Management and the Role of Spot Price Predictions in the Australian Retail Electricity Market

Author

Listed:
  • Stevenson Maxwell J

    (The University of Sydney)

  • Moreira do Amaral Luiz Felipe

    (Pontificia Universidade Catolica Do Rio De Janeiro)

  • Peat Maurice

    (The University of Sydney)

Abstract

This study investigates the extent to which predicted electricity spot prices from a statistical model, along with consensus forecasts issued by the Australian Financial Market Association (AFMA), provide unbiased price estimates of a forward contract price over a specified time to expiration. The statistical model is a regime switching time series model which is based on the dynamics of the market mechanism. To evaluate a price estimate, two criteria are utilized in order to conclude appropriateness for use in the marking-to-market process. First is the requirement that the predicted prices converge to the spot price at expiration of a hedging contract. The second criterion refers to the mis-pricing due to the price estimates over the days leading up to the contract expiration. Over the data period under consideration, the ranking of alternatives for generating price predictions is clear. On both criteria the Stevenson (2001) model is preferred. Of significance is the lack of support for the consensus (market) prices. They do not converge to the spot price at equilibrium and, further, they generate a considerable overvaluation of the risk management portfolio.

Suggested Citation

  • Stevenson Maxwell J & Moreira do Amaral Luiz Felipe & Peat Maurice, 2006. "Risk Management and the Role of Spot Price Predictions in the Australian Retail Electricity Market," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 10(3), pages 1-25, September.
    • Handle: RePEc:bpj:sndecm:v:10:y:2006:i:3:n:4
    DOI: 10.2202/1558-3708.1383
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    References listed on IDEAS

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    Cited by:

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    2. Janczura, Joanna & Trück, Stefan & Weron, Rafał & Wolff, Rodney C., 2013. "Identifying spikes and seasonal components in electricity spot price data: A guide to robust modeling," Energy Economics, Elsevier, vol. 38(C), pages 96-110.
    3. Yan, Guan & Trück, Stefan, 2020. "A dynamic network analysis of spot electricity prices in the Australian national electricity market," Energy Economics, Elsevier, vol. 92(C).
    4. Jakub Nowotarski & Jakub Tomczyk & Rafal Weron, 2013. "Modeling and forecasting of the long-term seasonal component of the EEX and Nord Pool spot prices," HSC Research Reports HSC/13/02, Hugo Steinhaus Center, Wroclaw University of Science and Technology.
    5. Nowotarski, Jakub & Tomczyk, Jakub & Weron, Rafał, 2013. "Robust estimation and forecasting of the long-term seasonal component of electricity spot prices," Energy Economics, Elsevier, vol. 39(C), pages 13-27.
    6. Weron, Rafał & Zator, Michał, 2015. "A note on using the Hodrick–Prescott filter in electricity markets," Energy Economics, Elsevier, vol. 48(C), pages 1-6.
    7. Tafakori, Laleh & Pourkhanali, Armin & Fard, Farzad Alavi, 2018. "Forecasting spikes in electricity return innovations," Energy, Elsevier, vol. 150(C), pages 508-526.
    8. Weron, Rafał, 2014. "Electricity price forecasting: A review of the state-of-the-art with a look into the future," International Journal of Forecasting, Elsevier, vol. 30(4), pages 1030-1081.
    9. John Foster & Liam Wagner & Ariel Liebman, 2011. "Market and Economic Modelling of the Intelligent Grid: 1st Interim Report 2009," Energy Economics and Management Group Working Papers 08, School of Economics, University of Queensland, Australia.

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