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Trade policy uncertainty, shipping risk, and commodity markets

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  • Shang, Mengya
  • Zhang, Lin
  • Duan, Hongcheng
  • Wang, Lizhi
  • Xiao, Nanyun

Abstract

This study examines the impact of trade policy uncertainty (TPU) on commodity market volatility through shipping risk. We decompose realized volatility into common and idiosyncratic components. Using the time-varying parameter vector autoregression Diebold–Yilmaz model, we explore the spillover effects of TPU and shipping risk and the connectedness of commodity market returns. Findings reveal that the connectedness is highest for common volatility, followed by realized volatility, and lowest for idiosyncratic volatility. TPU has notable net spillover effects on all types of volatility. However, shipping risk has notable net spillover effects only on idiosyncratic volatility. We also demonstrate that TPU directly impacts realized volatility and common volatility in the commodity market. By contrast, for idiosyncratic volatility, TPU indirectly affects the commodity market through shipping risk.

Suggested Citation

  • Shang, Mengya & Zhang, Lin & Duan, Hongcheng & Wang, Lizhi & Xiao, Nanyun, 2025. "Trade policy uncertainty, shipping risk, and commodity markets," Finance Research Letters, Elsevier, vol. 73(C).
    • Handle: RePEc:eee:finlet:v:73:y:2025:i:c:s1544612324016337
    DOI: 10.1016/j.frl.2024.106604
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    1. Serdar Ongan & Ismet Gocer, 2020. "Does trade policy related uncertainty affect international trade? Evidence from the US-China commodity trade," China Economic Journal, Taylor & Francis Journals, vol. 13(3), pages 364-375, September.
    2. Diebold, Francis X. & Yılmaz, Kamil, 2014. "On the network topology of variance decompositions: Measuring the connectedness of financial firms," Journal of Econometrics, Elsevier, vol. 182(1), pages 119-134.
    3. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
    4. Cheng-Wen Chang & Ming-Hsien Hsueh & Chia-Nan Wang & Cheng-Chun Huang, 2023. "Exploring the Factors Influencing the Impact of the COVID-19 Pandemic on Global Shipping: A Case Study of the Baltic Dry Index," Sustainability, MDPI, vol. 15(14), pages 1-17, July.
    5. Mei, Dexiang & Xie, Yutang, 2022. "U.S. grain commodity futures price volatility: Does trade policy uncertainty matter?," Finance Research Letters, Elsevier, vol. 48(C).
    6. Sun, Ting-Ting & Su, Chi-Wei & Mirza, Nawazish & Umar, Muhammad, 2021. "How does trade policy uncertainty affect agriculture commodity prices?," Pacific-Basin Finance Journal, Elsevier, vol. 66(C).
    7. Scott R. Baker & Nicholas Bloom & Steven J. Davis, 2016. "Measuring Economic Policy Uncertainty," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 131(4), pages 1593-1636.
    8. Herskovic, Bernard & Kelly, Bryan & Lustig, Hanno & Van Nieuwerburgh, Stijn, 2016. "The common factor in idiosyncratic volatility: Quantitative asset pricing implications," Journal of Financial Economics, Elsevier, vol. 119(2), pages 249-283.
    9. Chen, Xiangyu & Tongurai, Jittima, 2024. "Price spillovers and interdependences in China's agricultural commodity futures market: Evidence from the US-China trade dispute," International Review of Economics & Finance, Elsevier, vol. 96(PA).
    10. Dahl, Roy Endré & Oglend, Atle & Yahya, Muhammad, 2020. "Dynamics of volatility spillover in commodity markets: Linking crude oil to agriculture," Journal of Commodity Markets, Elsevier, vol. 20(C).
    11. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Paul Labys, 2003. "Modeling and Forecasting Realized Volatility," Econometrica, Econometric Society, vol. 71(2), pages 579-625, March.
    12. Alexei Onatski, 2010. "Determining the Number of Factors from Empirical Distribution of Eigenvalues," The Review of Economics and Statistics, MIT Press, vol. 92(4), pages 1004-1016, November.
    13. Sugimoto, Kimiko & Matsuki, Takashi & Yoshida, Yushi, 2014. "The global financial crisis: An analysis of the spillover effects on African stock markets," Emerging Markets Review, Elsevier, vol. 21(C), pages 201-233.
    14. Giorgio E. Primiceri, 2005. "Time Varying Structural Vector Autoregressions and Monetary Policy," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 72(3), pages 821-852.
    15. Torben G. Andersen & Viktor Todorov, 2009. "Realized Volatility and Multipower Variation," CREATES Research Papers 2009-49, Department of Economics and Business Economics, Aarhus University.
    16. Stock, J.H. & Watson, M.W., 2016. "Dynamic Factor Models, Factor-Augmented Vector Autoregressions, and Structural Vector Autoregressions in Macroeconomics," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 415-525, Elsevier.
    17. Akyildirim, Erdinc & Cepni, Oguzhan & Molnár, Peter & Uddin, Gazi Salah, 2022. "Connectedness of energy markets around the world during the COVID-19 pandemic," Energy Economics, Elsevier, vol. 109(C).
    18. Chen, Yanhua & Pantelous, Athanasios A., 2022. "The U.S.-China trade conflict impacts on the Chinese and U.S. stock markets: A network-based approach," Finance Research Letters, Elsevier, vol. 46(PB).
    19. Diebold, Francis X. & Yilmaz, Kamil, 2012. "Better to give than to receive: Predictive directional measurement of volatility spillovers," International Journal of Forecasting, Elsevier, vol. 28(1), pages 57-66.
    20. Rui Da & Dacheng Xiu, 2021. "When Moving‐Average Models Meet High‐Frequency Data: Uniform Inference on Volatility," Econometrica, Econometric Society, vol. 89(6), pages 2787-2825, November.
    21. JEBABLI, Ikram & KOUAISSAH, Noureddine & AROURI, Mohamed, 2022. "Volatility Spillovers between Stock and Energy Markets during Crises: A Comparative Assessment between the 2008 Global Financial Crisis and the Covid-19 Pandemic Crisis," Finance Research Letters, Elsevier, vol. 46(PA).
    22. Nikolaos Antonakakis & Ioannis Chatziantoniou & David Gabauer, 2020. "Refined Measures of Dynamic Connectedness based on Time-Varying Parameter Vector Autoregressions," JRFM, MDPI, vol. 13(4), pages 1-23, April.
    23. Lin, Faqin & Sim, Nicholas C.S., 2013. "Trade, income and the Baltic Dry Index," European Economic Review, Elsevier, vol. 59(C), pages 1-18.
    24. Marco Del Negro & Giorgio E. Primiceri, 2015. "Time Varying Structural Vector Autoregressions and Monetary Policy: A Corrigendum," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 82(4), pages 1342-1345.
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    1. Chen, Shuiyang & Meng, Bin & Qiu, Bingcheng & Kuang, Haibo, 2025. "Dynamic effects of maritime risk on macroeconomic and global maritime economic activity," Transport Policy, Elsevier, vol. 167(C), pages 246-263.

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