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Spillover effects and dynamic correlations between spot and forward tanker freight markets

Author

Listed:
  • Kevin X. Li
  • Guanqiu Qi
  • Wenming Shi
  • Zhongzhi Yang
  • Hee-Seok Bang
  • Su-Han Woo
  • Tsz Leung Yip

Abstract

Monitoring and analysing information transmission across different shipping markets is an important tool for participants to predict shipping freight rates, design portfolio investments and manage freight rate risks. The purpose of this article is to investigate spillover effects and dynamic correlations between shipping spot and derivatives markets (tanker forward freight agreement, FFA) under the multivariate generalized autoregressive conditional heteroscedasticity framework. Empirical results show that spillovers in returns are unilateral from one-month FFA to spot markets, while they are bilateral between one-month and two-month FFA markets. However, insignificant mean spillovers between spot and two-month FFA markets are found. Volatility spillover effects among one-month FFA, two-month FFA and spot freight markets are bilateral. By analysing the correlation between different markets, highly persistent and significantly volatile correlations are found. Moreover, time-varying correlations between one-month and two-month FFA markets are much higher than those of between spot and each FFA market. Results from this article will be helpful to improve participants' predictions of return, volatility and correlation, which are significant for determining hedge strategies. In addition, the management of freight rate risk and portfolio investment can also benefit from the empirical results obtained in this article.

Suggested Citation

  • Kevin X. Li & Guanqiu Qi & Wenming Shi & Zhongzhi Yang & Hee-Seok Bang & Su-Han Woo & Tsz Leung Yip, 2014. "Spillover effects and dynamic correlations between spot and forward tanker freight markets," Maritime Policy & Management, Taylor & Francis Journals, vol. 41(7), pages 683-696, December.
    • Handle: RePEc:taf:marpmg:v:41:y:2014:i:7:p:683-696
    DOI: 10.1080/03088839.2014.958585
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    Cited by:

    1. Zaili Yang & Esin Erol Mehmed, 2019. "Artificial neural networks in freight rate forecasting," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 21(3), pages 390-414, September.
    2. Jingbo Yin & Meifeng Luo & Lixian Fan, 2017. "Dynamics and interactions between spot and forward freights in the dry bulk shipping market," Maritime Policy & Management, Taylor & Francis Journals, vol. 44(2), pages 271-288, February.
    3. 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).
    4. Alexandridis, George & Kavussanos, Manolis G. & Kim, Chi Y. & Tsouknidis, Dimitris A. & Visvikis, Ilias D., 2018. "A survey of shipping finance research: Setting the future research agenda," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 115(C), pages 164-212.
    5. Ziaul Haque Munim & Hans-Joachim Schramm, 0. "Forecasting container freight rates for major trade routes: a comparison of artificial neural networks and conventional models," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 0, pages 1-18.
    6. Regli, Frederik & Adland, Roar, 2019. "Crude oil contango arbitrage and the floating storage decision," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 122(C), pages 100-118.
    7. Ziaul Haque Munim & Hans-Joachim Schramm, 2017. "Forecasting container shipping freight rates for the Far East – Northern Europe trade lane," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 19(1), pages 106-125, March.
    8. 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).
    9. Maitra, Debasish & Rehman, Mobeen Ur & Dash, Saumya Ranjan & Kang, Sang Hoon, 2021. "Oil price volatility and the logistics industry: Dynamic connectedness with portfolio implications," Energy Economics, Elsevier, vol. 102(C).
    10. Shi, Wenming & Gong, Yuting & Yin, Jingbo & Nguyen, Son & Liu, Qian, 2022. "Determinants of dynamic dependence between the crude oil and tanker freight markets: A mixed-frequency data sampling copula model," Energy, Elsevier, vol. 254(PB).
    11. Angelopoulos, Jason & Sahoo, Satya & Visvikis, Ilias D., 2020. "Commodity and transportation economic market interactions revisited: New evidence from a dynamic factor model," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 133(C).
    12. Yang, Jialin & Ge, Ying-En & Li, Kevin X., 2022. "Measuring volatility spillover effects in dry bulk shipping market," Transport Policy, Elsevier, vol. 125(C), pages 37-47.
    13. 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.
    14. Gu, Yimiao & Chen, Zhenxi & Lien, Donald & Luo, Meifeng, 2020. "Quantile hedge ratio for forward freight market," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 138(C).
    15. Alexandridis, George & Sahoo, Satya & Song, Dong-Wook & Visvikis, Ilias, 2018. "Shipping risk management practice revisited: A new portfolio approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 110(C), pages 274-290.
    16. 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.
    17. 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).
    18. Ziaul Haque Munim & Hans-Joachim Schramm, 2021. "Forecasting container freight rates for major trade routes: a comparison of artificial neural networks and conventional models," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 23(2), pages 310-327, June.

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