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Combination hedging strategies for crude oil and dry bulk freight rates on the impacts of dynamic cross-market interaction

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  • Xiaolin Sun
  • Hailong Liu
  • Shiyuan Zheng
  • Shun Chen

Abstract

The fluctuation of freight rates revenue and the fierce volatility of oil cost are two of the most key risk exposures in the shipping industry. However, neglecting the dynamic interrelationship between the cost and the revenue markets leads to the overestimation or underestimation of hedging ratios, which makes the present single hedge strategy less efficient. This paper proposes an optimal combination hedging model for a shipowner trading the derivatives of crude oil and dry bulk freight rates simultaneously with the cross-market economic linkages. We investigate the impacts of spillover transmission, structural breaks, and dynamic conditional correlations (DCCs) on the optimal combination hedging trading. It is found that the significant volatility transmission between oil future and dry bulk forward freight agreements suggests a high dependence of the Capesize sector on the oil fluctuations, which means that the dynamic cross-market interactions have significant impacts on the aggregate risk exposures. Furthermore, the DCCs incorporating structural breaks significantly improve the performance of the combination hedge, which is superior to the two separate hedging strategies. Our study offers new insights into how the freight rates and oil markets relate to a combination hedging, which can be used to promote the risk management in the market.

Suggested Citation

  • Xiaolin Sun & Hailong Liu & Shiyuan Zheng & Shun Chen, 2018. "Combination hedging strategies for crude oil and dry bulk freight rates on the impacts of dynamic cross-market interaction," Maritime Policy & Management, Taylor & Francis Journals, vol. 45(2), pages 174-196, February.
    • Handle: RePEc:taf:marpmg:v:45:y:2018:i:2:p:174-196
    DOI: 10.1080/03088839.2017.1418092
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    Cited by:

    1. Yu, Fangping & Xiang, Zhiyuan & Wang, Xuanhe & Yang, Mo & Kuang, Haibo, 2023. "An innovative tool for cost control under fragmented scenarios: The container freight index microinsurance," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 169(C).
    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. Sun, Xiaolei & Liu, Chang & Wang, Jun & Li, Jianping, 2020. "Assessing the extreme risk spillovers of international commodities on maritime markets: A GARCH-Copula-CoVaR approach," International Review of Financial Analysis, Elsevier, vol. 68(C).
    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. Liu, Ke & Fu, Qiang, 2024. "How does geopolitical risk affect international freight?," Journal of Air Transport Management, Elsevier, vol. 118(C).
    6. Adewuyi, Adeolu O. & Adeleke, Musefiu A. & Tiwari, Aviral Kumar & Aikins Abakah, Emmanuel Joel, 2023. "Dynamic linkages between shipping and commodity markets: Evidence from a novel asymmetric time-frequency method," Resources Policy, Elsevier, vol. 83(C).
    7. Ashfaq, Saleha & Tang, Yong & Maqbool, Rashid, 2019. "Volatility spillover impact of world oil prices on leading Asian energy exporting and importing economies’ stock returns," Energy, Elsevier, vol. 188(C).
    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. Gu, Bingmei & Liu, Jiaguo, 2022. "Determinants of dry bulk shipping freight rates: Considering Chinese manufacturing industry and economic policy uncertainty," Transport Policy, Elsevier, vol. 129(C), pages 66-77.
    10. 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).
    11. 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.
    12. Meng, Bin & Wei, Bangguo & Yang, Mo & Kuang, Haibo, 2023. "Measuring the time-frequency spillover effect among carbon markets and shipping energy markets: A global perspective," Energy Economics, Elsevier, vol. 128(C).
    13. Su, Kuangxi & Yao, Yinhong & Zheng, Chengli & Xie, Wenzhao, 2023. "A novel hybrid strategy for crude oil future hedging based on the combination of three minimum-CVaR models," International Review of Economics & Finance, Elsevier, vol. 83(C), pages 35-50.
    14. Mhd Ruslan, Siti Marsila & Mokhtar, Kasypi, 2021. "Stock market volatility on shipping stock prices: GARCH models approach," The Journal of Economic Asymmetries, Elsevier, vol. 24(C).

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