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Forecasting spot rates at main routes in the dry bulk market

Author

Listed:
  • Shun Chen

    (Department of International Shipping, Shanghai Maritime University, Shanghai, PR China.)

  • Hilde Meersman

    (Department of Transport and Regional Economics, University of Antwerp, Antwerp, Belgium.)

  • Eddy van de Voorde

    (Department of Transport and Regional Economics, University of Antwerp, Antwerp, Belgium.)

Abstract

The dry bulk shipping market is a major component of the international shipping market and it is characterized by high risk and volatility, in view of the uncertainty caused by factors such as the global economy, the volume and pattern of seaborne trade, and government policies. In such markets, to model price behavior (of spot- or time charter rates) has always been a topic of great interest among researchers. This article makes an attempt to forecast spot rates at main routes for three types of dry bulk vessels and to find superior forecasting models that can provide better forecasts. In this article, 1-month change in the Baltic Index, representing the market sentiment, is firstly invented and incorporated into the forecasting models, and this indicator is found to be very helpful in improving prediction performance. Furthermore, some significant exogenous variables are also employed to improve forecasting performance. The results of the cointegration test reveal that there are no long-run relationships of spot prices between trading routes for all three ship sizes. Hence, except a vector error correction model, time series models, such as the ARIMA, ARIMAX, VAR and VARX, are employed in this article to make the prediction. All spot prices cover the period from January 1990 to December 2010, which is split into an estimation period and an out-of-sample forecasting period. In order to test whether the market since 2003 is significantly different from the market before, the in-sample estimation is made over two sample periods. Various models are estimated firstly over the whole period from January 1990 to June 2009, and then estimated again over the second period from January 2003 to June 2009 at all routes for three ship sizes. The period from July 2009 to December 2010 is then used to evaluate independent out-of-sample forecasts. The forecasting performance of various forecasting models is evaluated and the comparison of the forecasting capabilities between various models provides useful information in the selection of superior forecasting models, which can yield better forecasting results.

Suggested Citation

  • Shun Chen & Hilde Meersman & Eddy van de Voorde, 2012. "Forecasting spot rates at main routes in the dry bulk market," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 14(4), pages 498-537, December.
  • Handle: RePEc:pal:marecl:v:14:y:2012:i:4:p:498-537
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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. Ziaul Haque Munim & Hans-Joachim Schramm, 0. "Forecasting container freight rates for major trade routes: a comparison of artificial neural networks and conventional models," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 0, pages 1-18.
    3. Lucía Inglada-Pérez & Pablo Coto-Millán, 2021. "A Chaos Analysis of the Dry Bulk Shipping Market," Mathematics, MDPI, vol. 9(17), pages 1-35, August.
    4. 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.
    5. Melike Bildirici & Işıl Şahin Onat & Özgür Ömer Ersin, 2023. "Forecasting BDI Sea Freight Shipment Cost, VIX Investor Sentiment and MSCI Global Stock Market Indicator Indices: LSTAR-GARCH and LSTAR-APGARCH Models," Mathematics, MDPI, vol. 11(5), pages 1-27, March.
    6. 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.
    7. 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.
    8. 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.
    9. Gu, Bingmei & Liu, Jiaguo, 2022. "Determinants of dry bulk shipping freight rates: Considering Chinese manufacturing industry and economic policy uncertainty," Transport Policy, Elsevier, vol. 129(C), pages 66-77.
    10. Machava, Agostinho, 2017. "The Macroeconomic Determinants of the Pass-Through from the Market Interest Rate to the Bank Lending Rate in Mozambique," Umeå Economic Studies 954, Umeå University, Department of Economics.
    11. 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.
    12. Wetzstein, Brian & Florax, Raymond & Foster, Kenneth & Binkley, James, 2021. "Transportation costs: Mississippi River barge rates," Journal of Commodity Markets, Elsevier, vol. 21(C).
    13. Georgios I. Papayiannis, 2022. "Static Hedging of Freight Risk under Model Uncertainty," Papers 2207.00862, arXiv.org.
    14. 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.
    15. 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.
    16. Ziaul Haque Munim & Hans-Joachim Schramm, 2021. "Forecasting container freight rates for major trade routes: a comparison of artificial neural networks and conventional models," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 23(2), pages 310-327, June.

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