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Dynamics and interdependencies among different shipping freight markets

Author

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  • Kevin X. Li
  • Yi Xiao
  • Shu-Ling Chen
  • Wei Zhang
  • Yuquan Du
  • Wenming Shi

Abstract

An appropriate description of freight rate behaviors is important to maritime forecasting and portfolio diversification in shipping freight markets. We employ general autoregressive conditional heteroscedasticity-copula models to capture the dynamics and interdependencies among shipping freight rates. Using weekly data from 5 January 2002 to 24 March 2018, our main findings are first, Granger causality tests confirm the presence of one-way causality running from the dry bulk and the clean tanker freight rate returns to the container and the dirty tanker freight rate returns, respectively. Second, volatility persistence exists in individual shipping freight market and, in particular, it is much less persistent in the clean tanker freight market. Third, nonlinear dynamic interdependencies among freight rate returns are captured by performing time-varying copulas. The results not only deepen our understanding of freight rate behaviors but also offer new insights into portfolio diversification and risk management in the shipping freight markets.

Suggested Citation

  • Kevin X. Li & Yi Xiao & Shu-Ling Chen & Wei Zhang & Yuquan Du & Wenming Shi, 2018. "Dynamics and interdependencies among different shipping freight markets," Maritime Policy & Management, Taylor & Francis Journals, vol. 45(7), pages 837-849, October.
    • Handle: RePEc:taf:marpmg:v:45:y:2018:i:7:p:837-849
    DOI: 10.1080/03088839.2018.1488187
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    Citations

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    Cited by:

    1. Ki-Hong Choi & Seong-Min Yoon, 2020. "Asymmetric Dependence between Oil Prices and Maritime Freight Rates: A Time-Varying Copula Approach," Sustainability, MDPI, vol. 12(24), pages 1-16, December.
    2. Shi, Wenming & Gong, Yuting & Yin, Jingbo & Nguyen, Son & Liu, Qian, 2022. "Determinants of dynamic dependence between the crude oil and tanker freight markets: A mixed-frequency data sampling copula model," Energy, Elsevier, vol. 254(PB).
    3. Angelopoulos, Jason & Sahoo, Satya & Visvikis, Ilias D., 2020. "Commodity and transportation economic market interactions revisited: New evidence from a dynamic factor model," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 133(C).
    4. Yang, Jialin & Ge, Ying-En & Li, Kevin X., 2022. "Measuring volatility spillover effects in dry bulk shipping market," Transport Policy, Elsevier, vol. 125(C), pages 37-47.
    5. 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.
    6. Gong, Yuting & Li, Kevin X. & Chen, Shu-Ling & Shi, Wenming, 2020. "Contagion risk between the shipping freight and stock markets: Evidence from the recent US-China trade war," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 136(C).

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