Forecasting bunker prices; A nonstationary, multivariate methodology

Abstract

This paper suggests a methodological approach for the forecasting of marine fuel prices. The prediction of the bunker prices is of outmost importance for operators, as bunker prices affect heavily the economic planning and financial viability of ventures and determine decisions related to compliance with regulations. A multivariate nonstationary stochastic model available in the literature is being retrieved, after appropriate adjustment and testing. The model belongs to the class of periodically correlated stochastic processes with annual periodic components. The time series are appropriately transformed to become Gaussian, and then are decomposed to deterministic seasonal characteristics (mean value and standard deviation) and a residual time series. The residual part is proved to be stationary and then is modeled as a Vector AutoRegressive Mooving Average (VARMA) process. Finally, using the methodology presented, forecasts of a tetra-variate and an octa-variate time series of bunker prices are produced and are in good agreement with actual values. The obtained results encourages further research and deeper investigation of the driving characters of the multivariate time series of bunker prices.

Keywords Bunker prices; Nonstationary; Multivariate time series; Vector autoregressive mooving average; Air emissions