Forecasting in inefficient commodity markets

摘要

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 onrnelements of the information set.rnDesign/methodology/approach - The paper uses the model to perform static and dynamicrnforecasts over different horizons and to compare its forecasting performance with a random walk andrna moving average model.rnFindings - The paper provides a study of hourly electricity prices using recent advances in thernfinancial econometrics literature.rnOriginality/value - The contribution of the paper is its use of models of changing volatility tornproperly identify the type of heteroscedasticity in the data-generation processes. This is of majorrnimportance in forecasting.