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Forecasting period charter rates of VLCC tankers through neural networks: A comparison of alternative approaches

Author

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  • André A P Santos

    (Departamento de Economia, Campus Universitário Reitor João David Ferreira Lima, Universidade Federal de Santa Catarina, CEP: 88040-970, Florianópolis, SC, Brazil)

  • Luciano N Junkes

    (PETROBRAS – Petróleo Brasileiro S.A., Av. República do Chile, 500 – Centro, CEP: 20031-170, Rio de Janeiro, RJ, Brazil)

  • Floriano C M Pires Jr

    (Universidade Federal do Rio de Janeiro, COPPE – Cidade Universitária, Centro de Tecnologia, Ilha do Fundão, CEP: 21945-970, Rio de Janeiro, RJ, Brazil)

Abstract

Volume-wise, seaborne crude oil represents close to 45 per cent of all internationally traded crude oil – thus remaining as the modern world primary source of energy. The usual focus in seaborne freight rate forecasting literature is the spot rate, whereas, on the other hand, a limited amount of literature has been directed towards period charter rates. To the same extent, there is a scarce amount of literature available dealing with the use of artificial neural networks (NNs) in forecasting seaborne transport market rates. This article focuses on applying NNs to period charter rates forecasting of very large crude carriers. The performance achieved for 1- and 3-year period charter rate time series by two different NN models (multi-layer perceptron and radial basis function (RBF)) is benchmarked against a more elementary performance delivered by an autoregressive integrated moving average (ARIMA) model. We find that NN modelling delivers encouraging end results outperforming the benchmark model (ARIMA). We can also point out that NN using RBFs delivers the best overall predictive performance.

Suggested Citation

  • André A P Santos & Luciano N Junkes & Floriano C M Pires Jr, 2014. "Forecasting period charter rates of VLCC tankers through neural networks: A comparison of alternative approaches," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 16(1), pages 72-91, March.
    • Handle: RePEc:pal:marecl:v:16:y:2014:i:1:p:72-91
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    Citations

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    Cited by:

    1. Zaili Yang & Esin Erol Mehmed, 2019. "Artificial neural networks in freight rate forecasting," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 21(3), pages 390-414, September.
    2. Theodore Syriopoulos & Michael Tsatsaronis & Ioannis Karamanos, 2021. "Support Vector Machine Algorithms: An Application to Ship Price Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 55-87, January.
    3. Payman Eslami & Kihyo Jung & Daewon Lee & Amir Tjolleng, 2017. "Predicting tanker freight rates using parsimonious variables and a hybrid artificial neural network with an adaptive genetic algorithm," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 19(3), pages 538-550, August.
    4. Christos Katris & Manolis G. Kavussanos, 2021. "Time series forecasting methods for the Baltic dry index," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(8), pages 1540-1565, December.
    5. Sangseop Lim & Chang-hee Lee & Won-Ju Lee & Junghwan Choi & Dongho Jung & Younghun Jeon, 2022. "Valuation of the Extension Option in Time Charter Contracts in the LNG Market," Energies, MDPI, vol. 15(18), pages 1-14, September.

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