Short-Term Oil Price Forecasting Based on State Space Model

Abstract

In competitive petroleum markets, oil price forecasting has always been an important strategic tool for oil producers and consumers to predict market behavior. In this study, we researched the monthly crude oil price in the period between 1988 and 2009. Firstly, we present a state space model to represent oil price system. Secondly, we determine the parameter estimates of the state space model for oil price through a faster algorithm to compute the likelihood function. Lastly, we use the Kalman filter method to estimate the next three months’ oil price and compare it with the econometric structure model as a benchmark. Empirical results indicate that the state space model performs well in terms of some standard statistics indices, and it may be a promising method for short-term oil price forecasting.