IDEAS home Printed from https://ideas.repec.org/a/gam/jjrfmx/v17y2024i6p240-d1411751.html
   My bibliography  Save this article

Resource Price Interconnections and the Impact of Geopolitical Shocks Using Granger Causality: A Case Study of Ukraine–Russia Unrest

Author

Listed:
  • Eirini Kostaridou

    (Department of Agricultural Development, School of Agricultural Science and Forestry, Democritus University of Thrace, 68200 Orestiada, Greece)

  • Nikolaos Siatis

    (Independent Researcher, 16344 Ilioupoli, Greece)

  • Eleni Zafeiriou

    (Department of Agricultural Development, School of Agricultural Science and Forestry, Democritus University of Thrace, 68200 Orestiada, Greece)

Abstract

Political events significantly impact economic indices, including agricultural commodities. While Granger causality is a well-established method for analyzing interdependencies between time series data, its traditional application can be challenging to interpret across multiple periods. This research enhances the Granger causality method to quantify changes in the interlinkages among variables over time, offering a more intuitive framework for analyzing how political events affect economic indices. The proposed method involves conducting Granger causality tests across different periods, forming vectors from the results to capture transitions from Granger-causing to non-Granger-causing variables. These vector amplitudes provide quantitative measures of changes with explanatory power over time. The dataset includes eight variables over a decade, focusing on the following major geopolitical events: the Russian occupation of Crimea in 2014 and the invasion of Ukraine in 2022, with an intermediate “no-shocks” period as the reference. The results show significant changes in the interlinkages among the variables during crisis periods compared to stable periods. This enhanced method provides valuable insights, informing trading strategies and risk management during periods of geopolitical instability. This innovative approach offers a novel tool for market participants to better understand and respond to economic shocks caused by political events.

Suggested Citation

  • Eirini Kostaridou & Nikolaos Siatis & Eleni Zafeiriou, 2024. "Resource Price Interconnections and the Impact of Geopolitical Shocks Using Granger Causality: A Case Study of Ukraine–Russia Unrest," JRFM, MDPI, vol. 17(6), pages 1-20, June.
    • Handle: RePEc:gam:jjrfmx:v:17:y:2024:i:6:p:240-:d:1411751
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1911-8074/17/6/240/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1911-8074/17/6/240/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Gkillas, Konstantinos & Gupta, Rangan & Pierdzioch, Christian, 2020. "Forecasting realized oil-price volatility: The role of financial stress and asymmetric loss," Journal of International Money and Finance, Elsevier, vol. 104(C).
    2. Koutroumanidis, Theodoros & Zafeiriou, Eleni & Arabatzis, Garyfallos, 2009. "Asymmetry in price transmission between the producer and the consumer prices in the wood sector and the role of imports: The case of Greece," Forest Policy and Economics, Elsevier, vol. 11(1), pages 56-64, January.
    3. Guo, Jin & Tanaka, Tetsuji, 2022. "Do biofuel production and financial speculation in agricultural commodities influence African food prices? New evidence from a TVP-VAR extended joint connectedness approach," Energy Economics, Elsevier, vol. 116(C).
    Full references (including those not matched with items on IDEAS)

    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. Christos Floros & Konstantinos Gkillas & Christoforos Konstantatos & Athanasios Tsagkanos, 2020. "Realized Measures to Explain Volatility Changes over Time," JRFM, MDPI, vol. 13(6), pages 1-19, June.
    2. 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.
    3. Sheng, Xin & Kim, Won Joong & Gupta, Rangan & Ji, Qiang, 2023. "The impacts of oil price volatility on financial stress: Is the COVID-19 period different?," International Review of Economics & Finance, Elsevier, vol. 85(C), pages 520-532.
    4. Riza Demirer & Rangan Gupta & Jacobus Nel & Christian Pierdzioch, 2020. "Effect of Rare Disaster Risks on Crude Oil: Evidence from El Nino from Over 140 Years of Data," Working Papers 2020104, University of Pretoria, Department of Economics.
    5. Hong, Yanran & Yu, Jize & Su, Yuquan & Wang, Lu, 2023. "Southern oscillation: Great value of its trends for forecasting crude oil spot price volatility," International Review of Economics & Finance, Elsevier, vol. 84(C), pages 358-368.
    6. Arabatzis, Garyfallos & Petridis, Konstantinos & Galatsidas, Spyros & Ioannou, Konstantinos, 2013. "A demand scenario based fuelwood supply chain: A conceptual model," Renewable and Sustainable Energy Reviews, Elsevier, vol. 25(C), pages 687-697.
    7. Li, Sisi & Khan, Sufyan Ullah & Yao, Yao & Chen, George S. & Zhang, Lin & Salim, Ruhul & Huo, Jiaying, 2022. "Estimating the long-run crude oil demand function of China: Some new evidence and policy options," Energy Policy, Elsevier, vol. 170(C).
    8. Bonato, Matteo & Gupta, Rangan & Lau, Chi Keung Marco & Wang, Shixuan, 2020. "Moments-based spillovers across gold and oil markets," Energy Economics, Elsevier, vol. 89(C).
    9. Alfeus, Mesias & Nikitopoulos, Christina Sklibosios, 2022. "Forecasting volatility in commodity markets with long-memory models," Journal of Commodity Markets, Elsevier, vol. 28(C).
    10. Liu, Min & Lee, Chien-Chiang, 2021. "Capturing the dynamics of the China crude oil futures: Markov switching, co-movement, and volatility forecasting," Energy Economics, Elsevier, vol. 103(C).
    11. Liang, Chao & Huynh, Luu Duc Toan & Li, Yan, 2023. "Market momentum amplifies market volatility risk: Evidence from China’s equity market," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 88(C).
    12. Gupta, Rangan & Ji, Qiang & Pierdzioch, Christian & Plakandaras, Vasilios, 2023. "Forecasting the conditional distribution of realized volatility of oil price returns: The role of skewness over 1859 to 2023," Finance Research Letters, Elsevier, vol. 58(PC).
    13. Seyed Safdar Hosseini & Zahra Alizadeh Khalifehmahaleh, 2013. "Market Structure and Price Adjustment in the Iranian Tea Market," Iranian Economic Review (IER), Faculty of Economics,University of Tehran.Tehran,Iran, vol. 18(2), pages 1-19, spring.
    14. Mohsin, Muhammad & Jamaani, Fouad, 2023. "A novel deep-learning technique for forecasting oil price volatility using historical prices of five precious metals in context of green financing – A comparison of deep learning, machine learning, an," Resources Policy, Elsevier, vol. 86(PA).
    15. Gkillas, Konstantinos & Gupta, Rangan & Pierdzioch, Christian & Yoon, Seong-Min, 2021. "OPEC news and jumps in the oil market," Energy Economics, Elsevier, vol. 96(C).
    16. Oumayma GHARBI & Yousra TRICHILI & Mouna BOUJELBENE ABBES, 2022. "Impact of the COVID-19 pandemic on the relationship between uncertainty factors, investor’s behavioral biases and the stock market reaction of US Fintech companies," Journal of Academic Finance, RED research unit, university of Gabes, Tunisia, vol. 13(1), pages 101-122, June.
    17. Arabatzis, Garyfallos & Klonaris, Stathis, 2009. "An analysis of Greek wood and wood product imports: Evidence from the linear quadratic aids," Forest Policy and Economics, Elsevier, vol. 11(4), pages 266-270, July.
    18. Hong, Yanran & Wang, Lu & Liang, Chao & Umar, Muhammad, 2022. "Impact of financial instability on international crude oil volatility: New sight from a regime-switching framework," Resources Policy, Elsevier, vol. 77(C).
    19. Zhong, Yufei & Chen, Xuesheng & Wang, Chengfang & Wang, Zhixian & Zhang, Yuchen, 2023. "The hedging performance of green bond markets in China and the U.S.: Novel evidence from cryptocurrency uncertainty," Energy Economics, Elsevier, vol. 128(C).
    20. Salisu, Afees A. & Gupta, Rangan & Demirer, Riza, 2022. "Global financial cycle and the predictability of oil market volatility: Evidence from a GARCH-MIDAS model," Energy Economics, Elsevier, vol. 108(C).

    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:gam:jjrfmx:v:17:y:2024:i:6:p:240-:d:1411751. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.