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A Comparison of Models for Forecasting the Baltic Freight Index: Box-Jenkins Revisited

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  • K P B Cullinane

    (*Hong Kong Polytechnic University)

  • K J Mason

    (⋄School of Aeronautics, Cranfield University)

  • M Cape

    (‡Centre for International Shipping and Transport, University of Plymouth)

Abstract

The Baltic Freight Index (BFI) is a widely recognised barometer of dry bulk freight rates. As such, its composition is monitored continuously. In 1993, all handy size trades were expunged from the BFI. This paper tests whether the change in the composition of the BFI has altered its underlying behaviour. This is achieved by applying a Box-Jenkins methodology to a BFI database covering a period following this pivotal change and comparing the properties of the resulting ARIMA model to those of a model previously estimated by applying the same methodology to data from an earlier period. On the basis of a range of criteria, the two models prove to be remarkably similar and the paper concludes that the behaviour of the BFI has not been radically altered even following this radical revision.International Journal of Maritime Economics (1999) 1, 15–39; doi:10.1057/ijme.1999.10

Suggested Citation

  • K P B Cullinane & K J Mason & M Cape, 1999. "A Comparison of Models for Forecasting the Baltic Freight Index: Box-Jenkins Revisited," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 1(2), pages 15-39, December.
    • Handle: RePEc:pal:marecl:v:1:y:1999:i:2:p:15-39
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    Cited by:

    1. Alexandridis, George & Kavussanos, Manolis G. & Kim, Chi Y. & Tsouknidis, Dimitris A. & Visvikis, Ilias D., 2018. "A survey of shipping finance research: Setting the future research agenda," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 115(C), pages 164-212.
    2. Saeed, Naima & Nguyen, Su & Cullinane, Kevin & Gekara, Victor & Chhetri, Prem, 2023. "Forecasting container freight rates using the Prophet forecasting method," Transport Policy, Elsevier, vol. 133(C), pages 86-107.
    3. Ziaul Haque Munim & Hans-Joachim Schramm, 2017. "Forecasting container shipping freight rates for the Far East – Northern Europe trade lane," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 19(1), pages 106-125, March.
    4. Batchelor, Roy & Alizadeh, Amir & Visvikis, Ilias, 2007. "Forecasting spot and forward prices in the international freight market," International Journal of Forecasting, Elsevier, vol. 23(1), pages 101-114.
    5. Miao Su & Keun Sik Park & Sung Hoon Bae, 2024. "A new exploration in Baltic Dry Index forecasting learning: application of a deep ensemble model," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 26(1), pages 21-43, March.
    6. Joan Mileski & Christopher Clott & Cassia Bomer Galvao & Taliese Laverne, 2020. "Technical analysis: the psychology of the market of dry bulk freight rates," Journal of Shipping and Trade, Springer, vol. 5(1), pages 1-15, December.
    7. Goulas, Lambros & Skiadopoulos, George, 2012. "Are freight futures markets efficient? Evidence from IMAREX," International Journal of Forecasting, Elsevier, vol. 28(3), pages 644-659.
    8. Spyros Makridakis & Andreas Merikas & Anna Merika & Mike G. Tsionas & Marwan Izzeldin, 2020. "A novel forecasting model for the Baltic dry index utilizing optimal squeezing," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(1), pages 56-68, January.
    9. 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.
    10. Zhang, X. & Chen, M.Y. & Wang, M.G. & Ge, Y.E. & Stanley, H.E., 2019. "A novel hybrid approach to Baltic Dry Index forecasting based on a combined dynamic fluctuation network and artificial intelligence method," Applied Mathematics and Computation, Elsevier, vol. 361(C), pages 499-516.

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