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Shipping Bunker Cost Risk Assessment and Management during the Coronavirus Oil Shock

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  • Tzeu-Chen Han

    (Department of Shipping and Transportation Management, National Penghu University of Science and Technology, Penghu 880011, Taiwan)

  • Chih-Min Wang

    (Department of Aviation and Maritime Transportation Management, Chang Jung Christian University, Tainan 71101, Taiwan)

Abstract

This research explores ways to develop a risk management strategy that enables shipping companies to reduce unnecessary fuel cost risks, fuel price fluctuations and improve financial management. Through the Monte Carlo method, the study makes use of the simulation of the conditional value-at-risk (CVaR) model. First, the VaR of various shipping-fuel-cost combination over a ten-year period is simulated. Then, through the most appropriate probability distribution test, it is found that most of the VaR of shipping fuel cost combination are in Beta–Arcsine distribution. In other words, the high-frequency data are concentrated at both tails (minimum and maximum) with high volatility. Therefore, the best strategy is to install scrubbers on existing ships to purify their exhaust gas and choose natural gas-based marine fuel for new ships. This will benefit the shipping companies significantly more compared to the use of low-sulfur fuel and choosing forward bunker agreements. Bunker swaps and options of bunker prices to hedging the risk of bunker cost raised in the end of Coronavirus oil shock, the strategy could help achieve the goal of risk management in the sustainable supply chain.

Suggested Citation

  • Tzeu-Chen Han & Chih-Min Wang, 2021. "Shipping Bunker Cost Risk Assessment and Management during the Coronavirus Oil Shock," Sustainability, MDPI, vol. 13(9), pages 1-12, April.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:9:p:4998-:d:546156
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    References listed on IDEAS

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    1. Yewen Gu & Stein W. Wallace & Xin Wang, 2017. "The Impact of Bunker Risk Management on CO2 Emissions in Maritime Transportation Under ECA Regulation," Springer Optimization and Its Applications, in: Didem Cinar & Konstantinos Gakis & Panos M. Pardalos (ed.), Sustainable Logistics and Transportation, pages 199-224, Springer.
    2. Pedrielli, Giulia & Lee, Loo Hay & Ng, Szu Hui, 2015. "Optimal bunkering contract in a buyer–seller supply chain under price and consumption uncertainty," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 77(C), pages 77-94.
    3. Ruili Sun & Tiefeng Ma & Shuangzhe Liu & Milind Sathye, 2019. "Improved Covariance Matrix Estimation for Portfolio Risk Measurement: A Review," JRFM, MDPI, vol. 12(1), pages 1-34, March.
    4. Amir H. Alizadeh & Nikos K. Nomikos, 2009. "Shipping Derivatives and Risk Management," Palgrave Macmillan Books, Palgrave Macmillan, number 978-0-230-23580-9, March.
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    2. Abdulaziz M. T. Alzayedi & Suresh Sampath & Pericles Pilidis, 2022. "Techno–Economic and Risk Evaluation of Combined Cycle Propulsion Systems in Large Container Ships," Energies, MDPI, vol. 15(14), pages 1-14, July.

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