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.
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.