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A wavelet and neural network model for the prediction of dry bulk shipping indices

Author

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  • Yordan Leonov

    (Department of Navigation, Transport Management and Waterways Preservation, Technical University of Varna, 1, Studentska str., Varna 9010, Bulgaria.)

  • Ventsislav Nikolov

    (Department of Computer Science and Engineering, Technical University of Varna, 1, Studentska str., Varna 9010, Bulgaria.)

Abstract

Shipping markets are typically volatile in nature, as manifest in the dynamics of freight rates. According to shipowners’ risk propensity, volatility-related decisions vary from what kind of contract (time/voyage charter) to engage in to whether to enter/exit the business. After a sharp drop in freight rates at the end of 2008, a discussion about appropriate risk management concepts and statistical tools is needed. Owing to the magnitude of investment required in shipping, any additional information regarding the future direction of market volatility is of the utmost importance. The ambition of this article is exactly the same: to study fluctuations in the freight rates of the Baltic Panamax route 2A and the Baltic Panamax route 3A, using a tool of analysis that is new to shipping economics: a hybrid model of wavelets and neural networks. The wavelet multiscale decomposition of time series reveals volatility dynamics across different time frequencies and will uncover patterns that will be used by neural networks for prediction.

Suggested Citation

  • Yordan Leonov & Ventsislav Nikolov, 2012. "A wavelet and neural network model for the prediction of dry bulk shipping indices," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 14(3), pages 319-333, September.
    • Handle: RePEc:pal:marecl:v:14:y:2012:i:3:p:319-333
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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. Bai, Xiwen, 2021. "Tanker freight rates and economic policy uncertainty: A wavelet-based copula approach," Energy, Elsevier, vol. 235(C).
    3. 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.
    4. 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.
    5. Wenming Shi & Kevin X. Li, 2017. "Themes and tools of maritime transport research during 2000-2014," Maritime Policy & Management, Taylor & Francis Journals, vol. 44(2), pages 151-169, February.
    6. 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.
    7. 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.
    8. 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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