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Filtering and Forecasting Spot Electricity Prices in the Increasingly Deregulated Australian Electricity Market

  • Max Stevenson

    (Discipline of Finance, University of Sydney)

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    Modelling and forecasting the volatile spot pricing process for electricity presents a number of challenges. For increasingly deregulated electricity markets, like that in the Australian state of New South Wales, there is need to price a range of derivative securities used for hedging. Any derivative pricing model that hopes to capture the pricing dynamics within this market must be able to cope with the extreme volatility of the observed spot prices. By applying wavelet analysis, we examine both the price and demand series at different time locations and levels of resolution to reveal and differentiate what is signal and what is noise. Further, we cleanse the data of leakage from the high frequency, mean reverting price spikes into the more fundamental levels of frequency resolution. As it is from these levels that we base the reconstruction of our filtered series, we need to ensure they are least contaminated by noise. Using the filtered data, we explore time series models as possible candidates for explaining the pricing process and evaluate their forecasting ability. These models include one from the threshold autoregressive (AR) model. What we find is that models from the TAR class produce forecasts that best appear to capture the mean and variance components of the actual data.

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    Paper provided by Quantitative Finance Research Centre, University of Technology, Sydney in its series Research Paper Series with number 63.

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    Length: 31 pages
    Date of creation: 01 Sep 2001
    Date of revision:
    Handle: RePEc:uts:rpaper:63
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    1. Domian, Dale L. & Louton, David A., 1995. "Business cycle asymmetry and the stock market," The Quarterly Review of Economics and Finance, Elsevier, vol. 35(4), pages 451-466.
    2. L. C. G. Rogers & S. E. Satchell, 2000. "Does the behaviour of the asset tell us anything about the option price formula? A cautionary tale," Applied Financial Economics, Taylor & Francis Journals, vol. 10(1), pages 37-39.
    3. Domian, Dale L. & Louton, David A., 1997. "A threshold autoregressive analysis of stock returns and real economic activity," International Review of Economics & Finance, Elsevier, vol. 6(2), pages 167-179.
    4. Ramsey James B. & Lampart Camille, 1998. "The Decomposition of Economic Relationships by Time Scale Using Wavelets: Expenditure and Income," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 3(1), pages 1-22, April.
    5. Bhardwaj, Ravinder K & Brooks, LeRoy D, 1993. "Dual Betas from Bull and Bear Markets: Reversal of the Size Effect," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 16(4), pages 269-83, Winter.
    6. Wiggins, James B, 1992. "Betas in Up and Down Markets," The Financial Review, Eastern Finance Association, vol. 27(1), pages 107-23, February.
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