Forecasting in inefficient commodity markets
Purpose – This paper set out to use an autoregressive conditional heteroscedasticity (ARCH)-type model to capture the time-varying conditional variance of Alberta electricity prices. This is of major importance in forecasting, since ARCH-type models allow the conditional variance to depend on elements of the information set. Design/methodology/approach – The paper uses the model to perform static and dynamic forecasts over different horizons and to compare its forecasting performance with a random walk and a moving average model. Findings – The paper provides a study of hourly electricity prices using recent advances in the financial econometrics literature. Originality/value – The contribution of the paper is its use of models of changing volatility to properly identify the type of heteroscedasticity in the data-generation processes. This is of major importance in forecasting.
Volume (Year): 36 (2009)
Issue (Month): 4 (September)
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