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The impact of crude oil price on the tanker market

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  • Wenming Shi
  • Zhongzhi Yang
  • Kevin X. Li

Abstract

The demand for crude oil transport services is a derived demand, which is dependent on international trade of crude oil and crude oil carriers transport crude oil from producer regions to consumer regions. The purpose of this study is to investigate the relationship between fluctuations in oil prices and the freight market (hereinafter referred to as ‘the tanker market’) using a structural vector autoregressive (SVAR) model. Referring to Kilian and Park's method of structural decomposition of fluctuations in crude oil prices, crude oil price shocks are classified into crude oil supply shocks and crude oil non-supply shocks in this study. Also, the response of the tanker market to different shocks is examined using the impulse response analysis. The empirical results provide evidence that crude oil supply shocks have significant effects on the contemporaneous tanker market. However, the impact of crude oil non-supply shocks is insignificant. In addition, the accumulated responses of the tanker market to crude oil non-supply shocks and crude oil supply shocks are positive but they are far lower than other shocks to the tanker market. Empirical results from this research are consistent with the fact of high crude oil tanker freight rates but low profits for tanker operators.

Suggested Citation

  • Wenming Shi & Zhongzhi Yang & Kevin X. Li, 2013. "The impact of crude oil price on the tanker market," Maritime Policy & Management, Taylor & Francis Journals, vol. 40(4), pages 309-322, July.
    • Handle: RePEc:taf:marpmg:v:40:y:2013:i:4:p:309-322
    DOI: 10.1080/03088839.2013.777981
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    Cited by:

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    2. Maitra, Debasish & Rehman, Mobeen Ur & Dash, Saumya Ranjan & Kang, Sang Hoon, 2021. "Oil price volatility and the logistics industry: Dynamic connectedness with portfolio implications," Energy Economics, Elsevier, vol. 102(C).
    3. 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.
    4. 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.
    5. Hu, Jun & Zhang, Yujie & Wu, Peng & Li, Huijia, 2022. "An analysis of the global fuel-trading market based on the visibility graph approach," Chaos, Solitons & Fractals, Elsevier, vol. 154(C).
    6. Gavriilidis, Konstantinos & Kambouroudis, Dimos S. & Tsakou, Katerina & Tsouknidis, Dimitris A., 2018. "Volatility forecasting across tanker freight rates: The role of oil price shocks," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 118(C), pages 376-391.
    7. Yu, Hongchu & Fang, Zhixiang & Lu, Feng & Murray, Alan T. & Zhang, Hengcai & Peng, Peng & Mei, Qiang & Chen, Jinhai, 2019. "Impact of oil price fluctuations on tanker maritime network structure and traffic flow changes," Applied Energy, Elsevier, vol. 237(C), pages 390-403.
    8. Maitra, Debasish & Chandra, Saurabh & Dash, Saumya Ranjan, 2020. "Liner shipping industry and oil price volatility: Dynamic connectedness and portfolio diversification," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 138(C).
    9. Regli, Frederik & Adland, Roar, 2019. "Crude oil contango arbitrage and the floating storage decision," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 122(C), pages 100-118.
    10. 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).
    11. 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.
    12. 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).
    13. 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.
    14. Bai, Xiwen & Lam, Jasmine Siu Lee, 2019. "A copula-GARCH approach for analyzing dynamic conditional dependency structure between liquefied petroleum gas freight rate, product price arbitrage and crude oil price," Energy Economics, Elsevier, vol. 78(C), pages 412-427.
    15. Zhang, Yi, 2018. "Investigating dependencies among oil price and tanker market variables by copula-based multivariate models," Energy, Elsevier, vol. 161(C), pages 435-446.
    16. Monge, Manuel & Romero Rojo, María Fátima & Gil-Alana, Luis Alberiko, 2023. "The impact of geopolitical risk on the behavior of oil prices and freight rates," Energy, Elsevier, vol. 269(C).
    17. Naixia Mou & Yanxin Xie & Tengfei Yang & Hengcai Zhang & Yoo Ri Kim, 2019. "The Impact of Slumping Oil Price on the Situation of Tanker Shipping along the Maritime Silk Road," Sustainability, MDPI, vol. 11(17), pages 1-16, September.

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