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Container liner freight index based on data from e-booking platforms

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  • Zhao Yifei
  • Zhang Dali
  • Tatsuo Yanagita

Abstract

This article proposes a framework for a daily container freight index (DCFI) and investigates a number of principles in the design of this type of indices. Based on a comparative analysis with the existing container freight indices, we explore a method of integrating the framework with the use of data from e-booking platforms and illustrate why the new index can provide more insightful information for shippers. We also apply the framework to have a daily Shanghai container freight index by combining data sources from the platforms linked to the Shanghai port. By implementing the index to a risk analysis problem, we use numerical results to show the DCFI’s potential position in real hedging problems for container liner markets.

Suggested Citation

  • Zhao Yifei & Zhang Dali & Tatsuo Yanagita, 2018. "Container liner freight index based on data from e-booking platforms," Maritime Policy & Management, Taylor & Francis Journals, vol. 45(6), pages 739-755, August.
    • Handle: RePEc:taf:marpmg:v:45:y:2018:i:6:p:739-755
    DOI: 10.1080/03088839.2018.1443224
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    Cited by:

    1. 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.
    2. 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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