Spillover transmission and return connectedness in the post-IMO 2020 ERA global VLSFO bunker market: Evidence from the Diebold–Yılmaz framework

Murat Pamık; Erdem Fikir; Abdullah Açık

doi:10.33714/masteb.1998672

Spillover transmission and return connectedness in the post-IMO 2020 ERA global VLSFO bunker market: Evidence from the Diebold–Yılmaz framework

Abstract

This study examined return connectedness and spillover transmission among ten major VLSFO bunker hubs in the post-IMO 2020 era. Using daily log return data from 12 June 2023 to 12 June 2026 and the Diebold–Yılmaz connectedness framework, the study identified the system-wide, directional, net, and pairwise spillovers across key VLSFO bunkering hubs. The baseline results show a Total Connectedness Index of 72.56%, indicating a highly integrated global VLSFO bunker market, and a substantial share of return variation is explained by inter-market spillovers. Singapore emerges as the dominant net transmitter, while Hong Kong, Fujairah, Rotterdam, and Houston also act as important transmission hubs. Santos appears as the largest net receiver, suggesting stronger exposure to shocks originating from other hubs. Pairwise and network results show that the Singapore–Hong Kong–Fujairah axis forms the core of global VLSFO spillover transmission, while the Houston–New York connection and Rotterdam-linked routes represent important regional spillover channels. A complementary 200-day rolling-window analysis further shows that total connectedness varies over time, ranging from 52.75% to 88.80%, indicating that the VLSFO spillover structure is dynamic rather than fixed. These findings provide useful implications for bunker procurement, fuel cost management, and maritime energy risk monitoring by identifying the main transmission hubs and receiver markets in the global VLSFO network.

Keywords

References

Açık, A., & Doymuş, M. (2022). The price behavior of the MGO bunker market: An integrated causality and interpretive structural modeling approach. Maritime Technology and Research, 4(2), 252561-252561. https://doi.org/10.33175/mtr.2022.252561
Alizadeh, A. H., & Nomikos, N. K. (2004) The efficiency of the forward bunker market. International Journal of Logistics Research and Applications, 7(3), 281-296. https://doi.org/10.1080/13675560412331298527
Aydin, N., Lee, H., & Mansouri, S. A. (2017). Speed optimization and bunkering in liner shipping in the presence of uncertain service times and time windows at ports. European Journal of Operational Research, 259(1), 143–154. https://doi.org/10.1016/j.ejor.2016.10.002
Chen, D., & Yang, D. (2026). Impact of external uncertainties on internal volatility spillovers among shipping freight markets. Maritime Policy & Management, 53(3), 327–350. https://doi.org/10.1080/03088839.2025.2511720
Chen, Y., Lu, J., & Ma, M. (2022). How does oil future price imply bunker price—Cointegration and prediction analysis. Energies, 15(10), 3630. https://doi.org/10.3390/en15103630
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
Diebold, F. X., & Yilmaz, K. (2014). On the network topology of variance decompositions: Measuring the connectedness of financial firms. Journal of Econometrics, 182(1), 119–134. https://doi.org/10.1016/j.jeconom.2014.04.012
Diebold, F. X., & Yilmaz, K. (2012). Better to give than to receive: Predictive directional measurement of volatility spillovers. International Journal of Forecasting, 28(1), 57–66. https://doi.org/10.1016/j.ijforecast.2011.02.006

Ghosh, I., & De, A. (2024). Maritime fuel price prediction of European ports using least square boosting and Facebook prophet: Additional insights from explainable artificial intelligence. Transportation Research Part E: Logistics and Transportation Review, 189, 103686. https://doi.org/10.1016/j.tre.2024.103686
Huang, Z., Ding, X., & Wang, Y. (2026). Risk spillover and network connectedness analysis of green financial and related financial markets: Evidence from China. Humanities and Social Sciences Communications, 13(1), 344. https://doi.org/10.1057/s41599-026-06706-1
Kim, K., Lim, S., Lee, C. H., Lee, W. J., Jeon, H., Jung, J., & Jung, D. (2022). Forecasting liquefied natural gas bunker prices using artificial neural network for procurement management. Journal of Marine Science and Engineering, 10(12), 1814. https://doi.org/10.3390/jmse10121814
Kocaman, B. E. (2026). The impact of fluctuations in global energy prices on maritime transportation: A frequency-based approach. Transactions on Maritime Science, 15(1), 007. https://doi.org/10.7225/toms.v15.n01.007
Li, X., & Yip, T. L. (2023). Dynamic interdependence and volatility spillovers across bunker fuel markets and shipping freight markets. Maritime Policy & Management, 50(3), 351–374. https://doi.org/10.1080/03088839.2021.2005265
Nilu, T. Y., Wang, C., & Ahmed, S. (2026). Connectedness and portfolio diversification among major bunker fuel market: The role of crude oil and geopolitical risk. Research in Transportation Economics, 117, 101744. https://doi.org/10.1016/j.retrec.2026.101744
Notteboom, T. E., & Vernimmen, B. (2009). The effect of high fuel costs on liner service configuration in container shipping. Journal of Transport Geography, 17(5), 325–337. https://doi.org/10.1016/j.jtrangeo.2008.05.003
Okasha, S. H., Wada, Y., & Shibasaki, R. (2026). Forecasting bunker price using deep learning and dimensionality reduction. Maritime Business Review, 11(3), 272-294. https://doi.org/10.1108/MABR-12-2024-0093
Okudan Soytürk, H., & Açık, A. (2024). Unraveling the link: Examining the influence of dollar strength on dirty tanker freight rates. Marine Science and Technology Bulletin, 13(4), 304-317. https://doi.org/10.33714/masteb.1549020
Ronen, D. (2011) The effect of oil price on containership speed and fleet size. Journal of the Operational Research Society, 62(1), 211-216. https://doi.org/10.1057/jors.2009.169
Ship & Bunker (2026). World VLSFO bunker prices. Retrieved on June 15, 2026, from https://shipandbunker.com/prices
Singh, V. K., & Kumar, P. (2026). Crude oil and soft commodities volatility spillover patterns and portfolio diversification strategies in times of oil crises. Financial Innovation, 12, 76. https://doi.org/10.1186/s40854-025-00851-3
Sun, X., Chen, J., & Liu, W. (2023). Joint maritime bunker hedging and operational consumption based on CVaR optimization. Journal of Marine Science and Engineering, 11(5), 894. https://doi.org/10.3390/jmse11050894
Syriopoulos, T., Roumpis, E., & Tsatsaronis, M. (2023). Hedging strategies in carbon emission price dynamics: implications for shipping markets. Energies, 16(17), 6396.
Wang, S., & Meng, Q. (2012). Liner ship route schedule design with sea contingency time and port time uncertainty. Transportation Research Part B: Methodological, 46(5), 615–633. https://doi.org/10.1016/j.trb.2012.01.003
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

Details

Primary Language

English

Subjects

Energy Economy On Ships, Marine Transportation, Marine Vehicles Management Engineering, Maritime Engineering (Other)

Journal Section

Research Article

Authors

Murat Pamık
0000-0003-3268-1368
Türkiye

Erdem Fikir ^*
0000-0003-0853-2303
Türkiye

Abdullah Açık
0000-0003-4542-9831
Türkiye

Early Pub Date

September 7, 2026

Publication Date

September 30, 2026

Submission Date

July 19, 2026

Acceptance Date

August 27, 2026

Published in Issue

Year 2026 Volume: 15 Number: 3

DOI

https://doi.org/10.33714/masteb.1998672

IZ

https://izlik.org/JA47ET92TG

Cite

RIS / Bibtex

APA

Pamık, M., Fikir, E., & Açık, A. (2026). Spillover transmission and return connectedness in the post-IMO 2020 ERA global VLSFO bunker market: Evidence from the Diebold–Yılmaz framework. Marine Science and Technology Bulletin, 15(3), 261-277. https://doi.org/10.33714/masteb.1998672

AMA

1.Pamık M, Fikir E, Açık A. Spillover transmission and return connectedness in the post-IMO 2020 ERA global VLSFO bunker market: Evidence from the Diebold–Yılmaz framework. Mar. Sci. Tech. Bull. 2026;15(3):261-277. doi:10.33714/masteb.1998672

Chicago

Pamık, Murat, Erdem Fikir, and Abdullah Açık. 2026. “Spillover Transmission and Return Connectedness in the Post-IMO 2020 ERA Global VLSFO Bunker Market: Evidence from the Diebold–Yılmaz Framework”. Marine Science and Technology Bulletin 15 (3): 261-77. https://doi.org/10.33714/masteb.1998672.

EndNote

Pamık M, Fikir E, Açık A (September 30, 2026) Spillover transmission and return connectedness in the post-IMO 2020 ERA global VLSFO bunker market: Evidence from the Diebold–Yılmaz framework. Marine Science and Technology Bulletin 15 3 261–277.

IEEE

[1]M. Pamık, E. Fikir, and A. Açık, “Spillover transmission and return connectedness in the post-IMO 2020 ERA global VLSFO bunker market: Evidence from the Diebold–Yılmaz framework”, Mar. Sci. Tech. Bull., vol. 15, no. 3, pp. 261–277, Sept. 2026, doi: 10.33714/masteb.1998672.

ISNAD

Pamık, Murat - Fikir, Erdem - Açık, Abdullah. “Spillover Transmission and Return Connectedness in the Post-IMO 2020 ERA Global VLSFO Bunker Market: Evidence from the Diebold–Yılmaz Framework”. Marine Science and Technology Bulletin 15/3 (September 30, 2026): 261-277. https://doi.org/10.33714/masteb.1998672.

JAMA

1.Pamık M, Fikir E, Açık A. Spillover transmission and return connectedness in the post-IMO 2020 ERA global VLSFO bunker market: Evidence from the Diebold–Yılmaz framework. Mar. Sci. Tech. Bull. 2026;15:261–277.

MLA

Pamık, Murat, et al. “Spillover Transmission and Return Connectedness in the Post-IMO 2020 ERA Global VLSFO Bunker Market: Evidence from the Diebold–Yılmaz Framework”. Marine Science and Technology Bulletin, vol. 15, no. 3, Sept. 2026, pp. 261-77, doi:10.33714/masteb.1998672.

Vancouver

1.Murat Pamık, Erdem Fikir, Abdullah Açık. Spillover transmission and return connectedness in the post-IMO 2020 ERA global VLSFO bunker market: Evidence from the Diebold–Yılmaz framework. Mar. Sci. Tech. Bull. 2026 Sep. 30;15(3):261-77. doi:10.33714/masteb.1998672