Forecasting in inefficient commodity markets

Abstract

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.

Citation

Gogas, P. and Serletis, A. (2009), "Forecasting in inefficient commodity markets", Journal of Economic Studies, Vol. 36 No. 4, pp. 383-392. https://doi.org/10.1108/01443580910973592