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Valuation of the Extension Option in Time Charter Contracts in the LNG Market

Author

Listed:
  • Sangseop Lim

    (College of Maritime Sciences, Korea Maritime & Ocean University, Busan 49112, Korea)

  • Chang-hee Lee

    (College of Maritime Sciences, Korea Maritime & Ocean University, Busan 49112, Korea)

  • Won-Ju Lee

    (College of Maritime Sciences, Korea Maritime & Ocean University, Busan 49112, Korea
    Interdisciplinary Major of Maritime AI Convergence, Korea Maritime and Ocean University, Busan 49112, Korea)

  • Junghwan Choi

    (Law School, Dalian Maritime University, No. 1 Liaoning Road, Dalian 116026, China)

  • Dongho Jung

    (Offshore Platform Research Division, Korea Research Institute of Ship and Ocean Engineering (KRISO), Daejeon 34103, Korea)

  • Younghun Jeon

    (Korea Marine Equipment Research Institute (KOMERI), Busan 46754, Korea)

Abstract

A rapid transition toward a decarbonized economy is underway, following the Paris Agreement and the International Maritime Organization 2030 decarbonization goals. However, due to the high cost of the rapid transition to eco-friendly energy and the geopolitical conflict in eastern Europe, liquefied natural gas (LNG), which emits less carbon than other fossil fuels, is gaining popularity. As the spot market grows due to increased LNG demand, the usage of period extension options in time charter (T/C) contracts is increasing; however, these options are generally provided free of charge in practice, without economic evaluation; this is because some shipowners want to make their time charter contracts more attractive to the more credible charterers. Essentially, the reason for why this option has not been evaluated is because there is no reliable evaluation model currently used in practice. That is, research on the evaluation model for the T/C extension option has been insufficient. Therefore, this study evaluates the economic value of the extended period option in LNG time charter contracts using machine learning models, such as artificial neural networks, support vector machines, and random forest, and then compares them with the Black–Scholes model that is used for option valuations in financial markets. The results indicate superior valuation performance of the random forest model compared with the other models; particularly, its performance was significantly better than the Black–Scholes model. Since T/C extension options involve significant sums in the balance sheets of both shipowners and charterers, the fair value of these options should be assessed. In this regard, this paper has meaning in proposing valid machine models to efficiently reflect the fair value of period extension options that are provided at no charge in the LNG market.

Suggested Citation

  • Sangseop Lim & Chang-hee Lee & Won-Ju Lee & Junghwan Choi & Dongho Jung & Younghun Jeon, 2022. "Valuation of the Extension Option in Time Charter Contracts in the LNG Market," Energies, MDPI, vol. 15(18), pages 1-14, September.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:18:p:6737-:d:915472
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    References listed on IDEAS

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    1. Adam Fadlalla & Chien-Hua Lin, 2001. "An Analysis of the Applications of Neural Networks in Finance," Interfaces, INFORMS, vol. 31(4), pages 112-122, August.
    2. Robert C. Merton, 2005. "Theory of rational option pricing," World Scientific Book Chapters, in: Sudipto Bhattacharya & George M Constantinides (ed.), Theory Of Valuation, chapter 8, pages 229-288, World Scientific Publishing Co. Pte. Ltd..
    3. Peter R. Hartley, 2015. "The Future of Long-term LNG Contracts," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3).
    4. André A P Santos & Luciano N Junkes & Floriano C M Pires Jr, 2014. "Forecasting period charter rates of VLCC tankers through neural networks: A comparison of alternative approaches," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 16(1), pages 72-91, March.
    5. Shuaiqiang Liu & Cornelis W. Oosterlee & Sander M. Bohte, 2019. "Pricing Options and Computing Implied Volatilities using Neural Networks," Risks, MDPI, vol. 7(1), pages 1-22, February.
    6. Jun Li & Michael G. Parsons, 1997. "Forecasting tanker freight rate using neural networks," Maritime Policy & Management, Taylor & Francis Journals, vol. 24(1), pages 9-30, January.
    7. Heesung Yun & Sangseop Lim & Kihwan Lee, 2018. "The value of options for time charterparty extension: an artificial neural networks (ANN) approach," Maritime Policy & Management, Taylor & Francis Journals, vol. 45(2), pages 197-210, February.
    8. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
    9. Black, Fischer & Scholes, Myron S, 1973. "The Pricing of Options and Corporate Liabilities," Journal of Political Economy, University of Chicago Press, vol. 81(3), pages 637-654, May-June.
    10. Mohamed M. Mostafa, 2004. "Forecasting the Suez Canal traffic: a neural network analysis," Maritime Policy & Management, Taylor & Francis Journals, vol. 31(2), pages 139-156, April.
    11. Kian-Guan Lim & Michelle Lim, 2020. "Financial performance of shipping firms that increase LNG carriers and the support of eco-innovation," Journal of Shipping and Trade, Springer, vol. 5(1), pages 1-25, December.
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