The price behavior of the MGO bunker market: An integrated causality and interpretive structural modeling approach
Keywords:Volatility, MGO, Bunker price, Marine fuel hubs, Hierarchical flow
Marine fuel price differences of supply centers have a significant effect on operators’ budgets, and so are closely monitored by traders, charterers, and shipowners. The aim of this study is to determine the volatility spillovers between the prices of the major fuel centers in the world and to form a hierarchical structure based on the influence and dependence powers of these centers. For this purpose, an integrated structure of causality in variance and Interpretive Structural Modeling (ISM) methods are used. The data set used in this study includes marine gas oil (MGO) prices for 8 fuel centers used extensively in the world, namely: Fujairah, Hong Kong, Houston, Istanbul, New York, Piraeus, Rotterdam, and Singapore. This data consists of 782 daily observations, covering a 3-year-period between 14.04.2017 and 13.04.2020. The ISM results reveal that these fuel centers lie at six different levels based on their driving and dependence powers, that the sources of price volatility in the market are Fujairah and Singapore, and that the center that is most affected by volatility is Piraeus. In addition to drawing a macro frame for the fuel market, the results obtained are thought to be useful in reducing risk in the market due to uncertainty for stakeholders.
Acik, A., & Baser, S. Ö. (2018). The reaction of vessel speeds to bunker price changes in dry bulk market. Transport & Logistics: The International Journal, 18(45), 17-25.
Acosta, M., Coronado, D., & Cerban, M. D. M. (2011). Bunkering competition and competitiveness at the ports of the Gibraltar Strait. Journal of Transport Geography, 19(4), 911-916. https://dx.doi.org/10.1016%2Fj.jtrangeo.2010.11.008
Alizadeh, A. H. (2013). Trading volume and volatility in the shipping forward freight market. Transportation Research Part E: Logistics and Transportation Review, 49(1), 250-265. https://doi.org/10.1016/j.tre.2012.08.001
Alizadeh, A. H., Kavussanos, M. G., & Menachof, D. A. (2004). Hedging against bunker price fluctuations using petroleum futures contracts: Constant versus time-varying hedge ratios. Applied Economics, 36(12), 1337-1353. https://doi.org/10.1080/0003684042000176801
Andersson, H., Fagerholt, K., & Hobbesland, K. (2015). Integrated maritime fleet deployment and speed optimization: Case study from RoRo shipping. Computers & Operations Research, 55, 233-240. https://doi.org/10.1016/j.cor.2014.03.017
Benali, N., & Feki, R. (2020). Evaluation of the relationship between freight transport, energy consumption, economic growth and greenhouse gas emissions: The VECM approach. Environment, Development and Sustainability, 22, 1039-1049. https://doi.org/10.1007/s10668-018-0232-x
Bildirici, M. E., & Turkmen, C. (2015). Nonlinear causality between oil and precious metals. Resources Policy, 46(2), 202-211. https://doi.org/10.1016/j.resourpol.2015.09.002
Brock, W., Dechert, W. D., & Scheinkman, J. (1987). A test for independence based on the correlation dimension. Working Paper, Department of Economics, University of Wisconsin, Madison.
Cariou, P., & Wolff, F. C. (2006). An analysis of bunker adjustment factors and freight rates in the Europe/Far East Market (2000-2004). Maritime Economics & Logistics, 8, 187-201. https://doi.org/10.1057/palgrave.mel.9100156
Cheung, Y. W., & Ng, L. K. (1996). A causality-in-variance test and its application to financial market prices. Journal of Econometrics, 72(1-2), 33-48. https://doi.org/10.1016/0304-4076(94)01714-X
Chuang, H. M., Lin, C. K., Chen, D. R., & Chen, Y. S. (2013). Evolving MCDM applications using hybrid expert-based ISM and DEMATEL models: An example of sustainable ecotourism. The Scientific World Journal, 2013, 751728. https://doi.org/10.1155/2013/751728
De, A., Choudhary, A., Turkay, M., & Tiwari, M. K. (2021). Bunkering policies for a fuel bunker management problem for liner shipping networks. European Journal of Operational Research, 289(3), 927-939. https://doi.org/10.1016/j.ejor.2019.07.044
Devanney, J. (2010). The impact of bunker price on VLCC spot rates. In Proceedings of the 3rd International Symposium on Ship Operations, Management and Economics. SNAME Greek Section, Athens Greece.
DHL. (2020). Bunker adjustment factor. DHL Logistics of Things. Retrieved from: https://lot.dhl.com/story/bunker-adjustment-factor
Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association, 74(366), 427-431. https://doi.org/10.2307/2286348
Doymuş, M., & Şakar, G. D. (2020). An exploratory study on the perceptions of stakeholders in LNG Bunkering Supply Chain. Journal of ETA Maritime Science, 8(1), 66-84. https://doi.org/10.5505/jems.2020.43255
EIA. (2019). The effects of changes to marine fuel sulfur limits in 2020 on energy markets. Energy Information Administration. Retrieved from https://www.eia.gov/outlooks/studies/imo/pdf/IMO.pdf
Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica: Journal of the Econometric Society, 50(4), 987-1007. https://doi.org/10.2307/1912773.
Fagerholt, K., Laporte, G., & Norstad, I. (2010). Reducing fuel emissions by optimizing speed on shipping routes. Journal of the Operational Research Society, 61, 523-529. https://doi.org/10.1057/jors.2009.77
Farina, F., Jensen, P. N., Plum, C. E. M., & Pisinger, D. (2014). Bunker purchasing with contracts. Maritime Economics & Logistics, 16(4), 418-435. https://doi.org/10.1057/mel.2014.7
Gilbert, C. L., & Morgan, C. W. (2011). Food price volatility (pp. 45-61). Piot-Lepetit, I., & M'Barek, R. (Eds.). Methods to Analyse Agricultural Commodity Price Volatility. Spain: Springer Press.
Hacihasanoglu, E., Simga-Mugan, F. N. C., & Soytas, U. (2012). Do global risk perceptions play a role in emerging market equity return volatilities? Emerging Markets Finance and Trade, 48(4), 67-78. https://doi.org/10.2753/REE1540-496X480404
Hafner, C. M., & Herwartz, H. (2006). A lagrange multiplier test for causality in variance. Economics Letters, 93(1), 137-141. https://doi.org/10.1016/j.econlet.2006.04.008
Hoffman, L. A. (2011). Using futures prices to forecast US corn prices: Model performance with increased price volatility (pp. 107-132). In Piot-Lepetit, I., & M'Barek, R. (Eds.). Methods to Analyse Agricultural Commodity Price Volatility. Spain: Springer Press.
Hong Kong Census and Statistics Department. (2019). Hong Kong energy statistics - 2018 Annual report 39. Retrieved from https://www.statistics.gov.hk/pub/B11000022018AN18B0100.pdf
Hsiao, Y. J., Chou, H. C. & Wu, C. C. (2014). Return lead-lag and volatility transmission in shipping freight markets. Maritime Policy & Management, 41(7), 697-714. https://doi.org/10.1080/03088839.2013.865849
Krishnaswamy, K. N., Sivakumar, A. I., & Mathirajan, M. (2009). Management research methodology: Integration of principles, methods and techniques. India: Pearson Education.
Kwiatkowski, D., Phillips, P. C. B., Schmidt, P., & Shin, Y. (1992). Testing the null hypothesis of stationarity against the alternative of a unit root. Journal of Econometrics, 54(1-3), 159-178. https://doi.org/10.1016/0304-4076(92)90104-Y
Lam, J. S. L., Chen, D., Cheng, F., & Wong, K. (2011). Assessment of the competitiveness of ports as bunkering hubs: empirical studies on Singapore and Shanghai. Transportation Journal, 50(2), 176-203. https://doi.org/10.5325/transportationj.50.2.0176
Luthra, S., Garg, D., & Haleem, A. (2015). An analysis of interactions among critical success factors to implement green supply chain management towards sustainability: An Indian perspective. Resources Policy, 46(1), 37-50. https://doi.org/10.1016/j.resourpol.2014.12.006
Månsson, K., & Shukur, G. (2009). Granger causality test in the presence of spillover effects. Communications in Statistics-Simulation and Computation, 38(10), 2039-2059. https://doi.org/10.1080/03610910903243695
Meyers, K. (2013). Liquid fuels infrastructure in northern New Jersey. Retrieved from http://www.nyc.gov/html/sirr/downloads/pdf/final_report/Ch_7_Liquid_Fuels_FINAL_singles.pdf
Mietzner, A. (2015). The Northern Sea Route: A comprehensive analysis (pp. 107-122). In Keupp, M. M. (Eds.). The Northern Sea Routes as an alternative container shipping route: A hypothetical question or a future growth path? Switzerland: Springer Gabler.
Nazlioglu, S., Erdem, C., & Soytas, U. (2013). Volatility spillover between oil and agricultural commodity markets. Energy Economics, 36(C), 658-665. https://doi.org/10.1016/j.eneco.2012.11.009
Nazlioglu, S., Gormuş, N. A., & Soytas, U. (2016). Oil prices and real estate investment trusts (REITs): Gradual-shift causality and volatility transmission analysis. Energy Economics, 60, 168-175. https://doi.org/10.1016/j.eneco.2016.09.009
Nouira, R., Amor, T. H., & Rault, C. (2019). Oil price fluctuations and exchange rate dynamics in the MENA region: Evidence from non-causality-in-variance and asymmetric non-causality tests. The Quarterly Review of Economics and Finance, 73(C), 159-171. https://doi.org/10.1016/j.qref.2018.07.011
Pedrielli, G., Lee, L. H., & Ng, S. H. (2015). Optimal bunkering contract in a buyer-seller supply chain under price and consumption uncertainty. Transportation Research Part E: Logistics and Transportation Review, 77, 77-94. https://doi.org/10.1016/j.tre.2015.02.010
PoR. (2020). Refining and chemicals | Port of Rotterdam. Retrieved from https://www.portofrotterdam.com/en/doing-business/setting-up/existing-industry/refining-and-chemicals
Sage, A. P. (1977). Interpretive structural modeling: Methodology for large-scale systems. New York: McGraw-Hill.
Sheng, X., Lee, L. H., & Chew, E. P. (2014). Dynamic determination of vessel speed and selection of bunkering ports for liner shipping under stochastic environment. OR Spectrum, 36(2), 455-480. https://doi.org/10.1007/s00291-012-0316-1
Ship and Bunker. (2020). MGO bunker prices. Retrieved from https://shipandbunker.com
Stefanakos, C., & Schinas, O. (2014). Forecasting bunker prices: A nonstationary, multivariate methodology. Transportation Research Part C: Emerging Technologies, 38, 177-194. https://doi.org/10.1016/j.trc.2013.11.017
Stefanakos, C., & Schinas, O. (2015). Fuzzy time series forecasting of bunker prices: Nonstationary considerations. WMU Journal of Maritime Affairs, 14, 177-199. https://doi.org/10.1007/s13437-015-0084-2
Stopford, M. (2009). Maritime Economics. London: Routledge.
Vilhelmsen, C., Lusby, R. M., & Larsen, J. (2013). Routing and scheduling in tramp shipping - Integrating bunker optimization: technical report. DTU Management Engineering. Retrieved from https://orbit.dtu.dk/en/publications/routing-and-scheduling-in-tramp-shipping-integrating-bunker-optim-2
Wang, D. H., Chen, C. C., & Lai, C. S. (2011). The rationale behind and effects of bunker adjustment factors. Journal of Transport Geography, 19(4), 467-474. https://doi.org/10.1016/j.jtrangeo.2009.11.002
Wang, Y., Yeo, G. T., & Ng, A. K. Y. (2014). Choosing optimal bunkering ports for liner shipping companies: A hybrid Fuzzy-Delphi-TOPSIS approach. Transport Policy, 35, 358-365. https://doi.org/10.1016/j.tranpol.2014.04.009
Yao, Z., Ng, S. H., & Lee, L. H. (2012). A study on bunker fuel management for the shipping liner services. Computers & Operations Research, 39(5), 1160-1172. https://doi.org/10.1016/j.cor.2011.07.012
Yudatama, U., Hidayanto, A. N., & Nazief, B. A. A. (2018). Approach using interpretive structural model (ISM) to determine key sub-factors at factors: Benefits, risk reductions, opportunities and obstacles in awareness IT Governance. Journal of Theoretical and Applied Information Technology, 96(16), 5537-5549.
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