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Valuing cargo flexibility in oil transportation

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  • Roar Adland
  • David Hansson
  • Levin von der Wense

Abstract

The seaborne oil transportation market is served by two main types of vessels—crude oil tankers and product tankers. Product tankers are designed to move refined oil products, yet they can also opportunistically carry ‘dirty’ products such as crude and heavy fuel oil, subject to the cost of tank cleaning when re-entering the clean products trade. We apply an entry-exit real option model with a stochastic freight rate differential to derive optimal triggers for switching between the two cargo types and estimate the value of the switching option. We show that the value of active switching has grown over time, and generally exceeds the additional construction cost of a product tanker. Our findings are important both from a practical point of view and for our understanding of market integration in the tanker freight market. Specifically, shipowners can use our model as a basis for optimizing chartering policy for clean product tankers. We also show that there are periods where the dirty market is persistently stronger, and discuss the possible reasons for such apparent inefficiencies.

Suggested Citation

  • Roar Adland & David Hansson & Levin von der Wense, 2017. "Valuing cargo flexibility in oil transportation," Maritime Policy & Management, Taylor & Francis Journals, vol. 44(7), pages 803-814, October.
    • Handle: RePEc:taf:marpmg:v:44:y:2017:i:7:p:803-814
    DOI: 10.1080/03088839.2017.1366079
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    References listed on IDEAS

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    Cited by:

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    2. Alexandridis, George & Kavussanos, Manolis G. & Kim, Chi Y. & Tsouknidis, Dimitris A. & Visvikis, Ilias D., 2018. "A survey of shipping finance research: Setting the future research agenda," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 115(C), pages 164-212.
    3. Yan, Zhaojin & Xiao, Yijia & Cheng, Liang & Chen, Song & Zhou, Xiao & Ruan, Xiaoguang & Li, Manchun & He, Rong & Ran, Bin, 2020. "Analysis of global marine oil trade based on automatic identification system (AIS) data," Journal of Transport Geography, Elsevier, vol. 83(C).
    4. Tvedt, Jostein, 2022. "Floating offshore wind and the real options to relocate," Energy Economics, Elsevier, vol. 116(C).
    5. Li, Yiliang & Bai, Xiwen & Wang, Qi & Ma, Zhongjun, 2022. "A big data approach to cargo type prediction and its implications for oil trade estimation," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 165(C).
    6. Konstantinos Drakos & Dimitris Tsouknidis, 2024. "Investment under uncertainty and irreversibility: Evidence from the shipping markets," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 29(2), pages 2139-2154, April.
    7. Sun, Xiaolin & Haralambides, Hercules & Liu, Hailong, 2019. "Dynamic spillover effects among derivative markets in tanker shipping," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 122(C), pages 384-409.

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