Forecasting gas component prices with multivariate structural time series models

Abstract

Predicting gas component prices over different horizons is important for both energy producers and consumers. In this study, we model and predict the joint dynamics of butanes, propane and naphtha traded in the north European market. Our approach is to use multivariate time series with unobservable components. We applied monthly data over a 10-year period, from 1995 to 2006, and tested the predictive power of fitted models using various hold out samples. The in-sample and out-of-sample results indicated that gas component prices follow stochastic processes with trend and autoregressive effects that continuously change over time while the seasonal patterns seem to be stationary. The prediction results were compared with random walk for one-step and multi-step forecasts in each of the out-of sample periods. The results are promising and indicate that our model can be used for short-/medium term forecasting of gas component prices.