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Return lead-lag and volatility transmission in shipping freight markets

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  • Yao-Jen Hsiao
  • Heng-Chih Chou
  • Chun-Chou Wu

Abstract

This study investigates the return lead - lag and volatility transmission between dry bulk shipping and container shipping freight markets over the period before, during and after the 2008 financial tsunami. Both cointegration analysis and the Granger causality test are applied to explore the lead - lag relationship between the Baltic dry index (BDI) and the China containerized freight index (CCFI). Besides, in the study we employed GARCH-BEKK model, which allows for transmission in freight volatility. On the whole, the empirical results show that the BDI reflects the economic climate earlier than the CCFI during the financial tsunami, whereas the CCFI leads the BDI after the financial tsunami. The price formation hypothesis could well explain the relationship. Moreover, volatility spillovers are found in most subperiods. The dynamics of the conditional volatilities differ, but causality links in the variance are found to be strong and bidirectional in normal periods, and unidirectional during the financial tsunami. Therefore, the occurrence of the financial tsunami could be regarded as an interference factor.

Suggested Citation

  • Yao-Jen Hsiao & Heng-Chih Chou & Chun-Chou Wu, 2014. "Return lead-lag and volatility transmission in shipping freight markets," Maritime Policy & Management, Taylor & Francis Journals, vol. 41(7), pages 697-714, December.
    • Handle: RePEc:taf:marpmg:v:41:y:2014:i:7:p:697-714
    DOI: 10.1080/03088839.2013.865849
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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. Bai, Xiwen & Lam, Jasmine Siu Lee, 2021. "Freight rate co-movement and risk spillovers in the product tanker shipping market: A copula analysis," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 149(C).
    3. 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.
    4. Gong, Yuting & Li, Kevin X. & Chen, Shu-Ling & Shi, Wenming, 2020. "Contagion risk between the shipping freight and stock markets: Evidence from the recent US-China trade war," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 136(C).
    5. Egor PLOTNIKOV & Aleksandr RAKHMANGULOV, 2021. "Modeling China'S Dry Port Cooperation In Supply Chains," Transport Problems, Silesian University of Technology, Faculty of Transport, vol. 16(3), pages 89-103, September.
    6. Wang, Chao & Kim, Yul-Seong & Kim, Chi Yeol, 2021. "Causality between logistics infrastructure and economic development in China," Transport Policy, Elsevier, vol. 100(C), pages 49-58.
    7. 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).
    8. Javier Población, 2017. "Are recent tanker freight rates stationary?," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 19(4), pages 650-666, December.

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