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Seasonality patterns in the container shipping freight rate market

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  • Jingbo Yin
  • Jinhao Shi

Abstract

Shipping indexes have attracted many researchers because they reflect the overall trend of corresponding seaborne markets and can provide implications for the future. Apart from the Baltic Dry Bulk Index (BDI) and correlated indices, the China Containerized Freight Index (CCFI) has been gaining more attention. As a country with large-scale manufacturing, China is an important exporting country and the CCFI was chosen to reflect the container shipping market because the data are more convincing and representative. There have been no systematic attempts to understand the seasonality patterns of container freights. Seasonality patterns reveal the regular fluctuation patterns within a 1-year period. They exist in time series, which are observed more than once a year, like the CCFI. To investigate the nature of seasonality (stochastic and/or deterministic) in container freight rates across different line services, we analyze the CCFI. This paper uses the HEGY method and Monte Carlo method comprehensively to figure out the small sample problem. In addition, seasonal dummy variables are used to test deterministic seasonality. Except for the Japan service series, which contains a half-year unit root, the other container freight rates seem to only involve a non-seasonal unit root at the zero frequency. Deterministic seasonality exists in all the line service series. Furthermore, the seasonality depends on the balance between supply and demand. Under this premise, the seasonal law of freight rates is much obvious.

Suggested Citation

  • Jingbo Yin & Jinhao Shi, 2018. "Seasonality patterns in the container shipping freight rate market," Maritime Policy & Management, Taylor & Francis Journals, vol. 45(2), pages 159-173, February.
    • Handle: RePEc:taf:marpmg:v:45:y:2018:i:2:p:159-173
    DOI: 10.1080/03088839.2017.1420260
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    Cited by:

    1. Sel, Burakhan & Minner, Stefan, 2022. "A hedging policy for seaborne forward freight markets based on probabilistic forecasts," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 166(C).
    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. 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.
    4. Zirui Liang & Ryuichi Shibasaki & Yuji Hoshino, 2021. "Do Foldable Containers Enhance Efficient Empty Container Repositioning under Demand Fluctuation?—Case of the Pacific Region," Sustainability, MDPI, vol. 13(9), pages 1-24, April.
    5. Zhou, Yusheng & Li, Xue & Yuen, Kum Fai, 2022. "Holistic risk assessment of container shipping service based on Bayesian Network Modelling," Reliability Engineering and System Safety, Elsevier, vol. 220(C).

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