IDEAS home Printed from https://ideas.repec.org/a/eee/finlet/v73y2025ics1544612324016337.html
   My bibliography  Save this article

Trade policy uncertainty, shipping risk, and commodity markets

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1544612324016337
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.frl.2024.106604?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    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. 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).
    6. Mei, Dexiang & Xie, Yutang, 2022. "U.S. grain commodity futures price volatility: Does trade policy uncertainty matter?," Finance Research Letters, Elsevier, vol. 48(C).
    7. 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).
    8. 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).
    9. 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.
    10. 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.
    11. 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).
    12. 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).
    13. 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.
    14. 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.
    15. 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.
    16. 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).
    17. 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.
    18. 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.
    19. 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.
    20. 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.
    21. Lin, Faqin & Sim, Nicholas C.S., 2013. "Trade, income and the Baltic Dry Index," European Economic Review, Elsevier, vol. 59(C), pages 1-18.
    22. Torben G. Andersen & Viktor Todorov, 2009. "Realized Volatility and Multipower Variation," CREATES Research Papers 2009-49, Department of Economics and Business Economics, Aarhus University.
    23. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Szafranek, Karol & Rubaszek, Michał & Uddin, Gazi Salah, 2024. "The role of uncertainty and sentiment for intraday volatility connectedness between oil and financial markets," Energy Economics, Elsevier, vol. 137(C).
    2. Shi, Huai-Long & Zhou, Wei-Xing, 2022. "Factor volatility spillover and its implications on factor premia," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 80(C).
    3. Youtao Xiang & Sumuya Borjigin, 2024. "High–low volatility spillover network between economic policy uncertainty and commodity futures markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 44(8), pages 1295-1319, August.
    4. Feng, Huiqun & Zhang, Jun & Guo, Na, 2023. "Time-varying linkages between energy and stock markets: Dynamic spillovers and driving factors," International Review of Financial Analysis, Elsevier, vol. 89(C).
    5. Rafael Baptista Palazzi & Ata Assaf & Marcelo Cabus Klotzle, 2024. "Dynamic connectedness between energy markets and the Brazilian cash market: An empirical analysis pre‐ and post‐COVID‐19," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 44(1), pages 27-56, January.
    6. Al-Shboul, Mohammad & Assaf, Ata & Mokni, Khaled, 2023. "Does economic policy uncertainty drive the dynamic spillover among traditional currencies and cryptocurrencies? The role of the COVID-19 pandemic," Research in International Business and Finance, Elsevier, vol. 64(C).
    7. Nguyen, Thao Thac Thanh & Pham, Son Duy & Li, Xiao-Ming & Do, Hung Xuan, 2024. "Does the U.S. export inflation? Evidence from the dynamic inflation spillover between the U.S. and EAGLEs," International Review of Economics & Finance, Elsevier, vol. 94(C).
    8. Ramesh, Shietal & Low, Rand Kwong Yew & Faff, Robert, 2025. "Corrigendum to “Modelling time-varying volatility spillovers across crises: Evidence from major commodity futures and the US stock market” [Energy Economics Volume 143, March 2025, 108225]," Energy Economics, Elsevier, vol. 147(C).
    9. Yanhong Feng & Dilong Xu & Pierre Failler & Tinghui Li, 2020. "Research on the Time-Varying Impact of Economic Policy Uncertainty on Crude Oil Price Fluctuation," Sustainability, MDPI, vol. 12(16), pages 1-24, August.
    10. Biswas, Priti & Jain, Prachi & Maitra, Debasish, 2024. "Are shocks in the stock markets driven by commodity markets? Evidence from Russia-Ukraine war," Journal of Commodity Markets, Elsevier, vol. 34(C).
    11. Zhou, Xiaoran & Enilov, Martin & Parhi, Mamata, 2024. "Does oil spin the commodity wheel? Quantile connectedness with a common factor error structure across energy and agricultural markets," Energy Economics, Elsevier, vol. 132(C).
    12. Li, Houjian & Li, Yanjiao & Luo, Fangyuan, 2025. "Unveiling the gold-oil whirl amidst market uncertainty shocks in China," The North American Journal of Economics and Finance, Elsevier, vol. 76(C).
    13. Assaf, Ata & Charif, Husni & Mokni, Khaled, 2021. "Dynamic connectedness between uncertainty and energy markets: Do investor sentiments matter?," Resources Policy, Elsevier, vol. 72(C).
    14. Kočenda, Evžen & Moravcová, Michala, 2024. "Frequency volatility connectedness and portfolio hedging of U.S. energy commodities," Research in International Business and Finance, Elsevier, vol. 69(C).
    15. Kumar, Pawan & Singh, Vipul Kumar & Rao, Sandeep, 2023. "Does the substitution effect lead to feedback effect linkage between ethanol, crude oil, and soft agricultural commodities?," Energy Economics, Elsevier, vol. 119(C).
    16. Banerjee, Ameet Kumar & Özer, Zeynep Sueda & Rahman, Molla Ramizur & Sensoy, Ahmet, 2024. "How does the time-varying dynamics of spillover between clean and brown energy ETFs change with the intervention of climate risk and climate policy uncertainty?," International Review of Economics & Finance, Elsevier, vol. 93(PA), pages 442-468.
    17. Juncal Cunado & David Gabauer & Rangan Gupta, 2024. "Realized volatility spillovers between energy and metal markets: a time-varying connectedness approach," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-17, December.
    18. Duan, Kun & Zhao, Yanqi & Urquhart, Andrew & Huang, Yingying, 2023. "Do clean and dirty cryptocurrencies connect with financial assets differently? The role of economic policy uncertainty," Energy Economics, Elsevier, vol. 127(PA).
    19. Cagli, Efe Caglar, 2023. "The volatility spillover between battery metals and future mobility stocks: Evidence from the time-varying frequency connectedness approach," Resources Policy, Elsevier, vol. 86(PA).
    20. Lei, Heng & Xue, Minggao & Ye, Jing, 2024. "The nexus between ReFi, carbon, fossil energy, and clean energy assets: Quantile time–frequency connectedness and portfolio implications," Energy Economics, Elsevier, vol. 132(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:finlet:v:73:y:2025:i:c:s1544612324016337. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/frl .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.