Geopolitical Shocks and Commodity Market Dynamics

  • Authors

    https://doi.org/10.14419/sj0tbc52

    Received date: August 6, 2025

    Accepted date: September 6, 2025

    Published date: December 5, 2025

  • Geopolitical Risk Index (GPR); Commodity Markets; Oil Volatility; Agricultural Commodities; Safe-Haven Assets; Machine Learning; ‎ Supply Chain Disruption; Sanctions; Speculation

  • Abstract

    This research comprehensively examines the effects of geopolitical risk (GPR) on global commodity markets and consolidates evidence ‎from 17 high-quality empirical studies published between 2010 and 2025. The SLR examines five thematic areas about geopolitical risk: (A) ‎a measurement of geopolitical risk, (B) the sensitivity of oil and energy markets, (C) Instability in agricultural and metal commodities, (D) ‎dynamics of safe-haven assets, and (E) the evolution of forecasting and modelling. The investigation critically examines how GPR ‎disruptions affect commodity markets. The literature establishes that geopolitical circumstances potentiate market space, redirect investment ‎flows, and create volatility in trade flows. The study recommends allowing for localised risk indices, integrated modelling architecture, and ‎cross-commodity portfolio analysis‎.

  • References

    1. Abdullah, M., Tiwari, A. K., Hossain, M. R., & Abakah, E. J. A. (2024). Geopolitical risk and firm-level environmental, social, and governance (ESG) performance. Journal of Environmental Management, 363. https://doi.org/10.1016/j.jenvman.2024.121245.
    2. Adekoya, O. B., Badmus, J. O., & Al-Faryan, M. A. S. (2025). Geopolitical Risks and the Predictability of Green Investments: A GARCH-Based Mixed Data Sampling Approach. International Journal of Finance and Economics. https://doi.org/10.1002/ijfe.3124.
    3. Ahmad, T., Madonski, R., Zhang, D., Huang, C., & Mujeeb, A. (2022). Data-driven probabilistic machine learning in sustainable smart energy/smart energy systems: Key developments, challenges, and future research opportunities in the context of smart grid paradigm. Renewable and Sustainable Energy Reviews, 160, 112128. https://doi.org/10.1016/j.rser.2022.112128.
    4. Ahmadian-Yazdi, F., Sokhanvar, A., Roudari, S., & Tiwari, A. K. (2025). Dynamics of the relationship between stock markets and exchange rates during quantitative easing and tightening. Financial Innovation, 11. https://doi.org/10.1186/s40854-024-00694-4.
    5. Aizenman, J., Lindahl, R., Stenvall, D., & Uddin, G. S. (2024). Geopolitical shocks and commodity market dynamics: New evidence from the Rus-sia-Ukraine conflict. European Journal of Political Economy, 85, 102574. https://doi.org/10.1016/j.ejpoleco.2024.102574.
    6. Alghamdi, H., & Alqithami, S. (2025). A robust machine learning framework for stock market classification. Expert Systems with Applications, 294, 128573. https://doi.org/10.1016/j.eswa.2025.128573.
    7. Alhnaity, B., & Abbod, M. (2020). A new hybrid financial time series prediction model. Engineering Applications of Artificial Intelligence, 95. https://doi.org/10.1016/j.engappai.2020.103873.
    8. Antonakakis, N., Chatziantoniou, I., & Gabauer, D. (2020). Refined Measures of Dynamic Connectedness based on Time-Varying Parameter Vector Autoregressions. Journal of Risk and Financial Management, 13(4), 84. https://doi.org/10.3390/jrfm13040084.
    9. Arezki, R., & Liu, Y. (2020). On the (Changing) asymmetry of global spillovers: Emerging markets vs. advanced economies. Journal of International Money and Finance, 107, 102219. https://doi.org/10.1016/j.jimonfin.2020.102219.
    10. Asim, M., Khan, M. Y., & Shafi, K. (2024). Investigation of herding behaviour using machine learning models. Review of Behavioural Finance, 16(3). https://doi.org/10.1108/RBF-05-2023-0121.
    11. Axelson, U., & Makarov, I. (2023). Informational Black Holes in Financial Markets. Journal of Finance, 78(6), 3099–3140. https://doi.org/10.1111/jofi.13270.
    12. Balcilar, M., Bekiros, S., & Gupta, R. (2017). The role of news-based uncertainty indices in predicting oil markets: a hybrid nonparametric quantile causality method. Empirical Economics, 53(3), 879–889. https://doi.org/10.1007/s00181-016-1150-0.
    13. Balcilar, M., Usman, O., & Agan, B. (2024). On the connectedness of commodity markets: A critical and selective survey of empirical studies and bib-liometric analysis. Journal of Economic Surveys, 38, 97–136. https://doi.org/10.1111/joes.12541.
    14. Bathla, G., Rani, R., & Aggarwal, H. (2023). Stocks of year 2020: prediction of high variations in stock prices using LSTM. Multimedia Tools and Applications, 82(7), 9727–9743. https://doi.org/10.1007/s11042-022-12390-5.
    15. Baur, D. G., & Lucey, B. M. (2010). Is gold a hedge or a safe haven? An analysis of stocks, bonds and gold. Financial Review, 45(2). https://doi.org/10.1111/j.1540-6288.2010.00244.x.
    16. Baur, D. G., & McDermott, T. K. (2010). Is gold a safe haven? International evidence. Journal of Banking and Finance, 34(8). https://doi.org/10.1016/j.jbankfin.2009.12.008.
    17. Bekiros, S., Boubaker, S., Nguyen, D. K., & Uddin, G. S. (2017). Black swan events and safe havens: The role of gold in globally integrated emerg-ing markets. Journal of International Money and Finance, 73, 317–334. https://doi.org/10.1016/j.jimonfin.2017.02.010.
    18. Bhattacharjee, A., Gaur, D., & Gupta, K. (2024). Russia–Ukraine war and the impact on Indian economy. Journal of Economic Studies, 51(4), 841–858. https://doi.org/10.1108/JES-03-2023-0136.
    19. Boubaker, H., & Larbi, O. Ben. (2022). Dynamic dependence and hedging strategies in BRICS stock markets with oil during crises. Economic Anal-ysis and Policy, 76, 263–279. https://doi.org/10.1016/j.eap.2022.08.011.
    20. Bouri, E., & Alsagr, N. (2024). Hedging investment-grade and high-yield bonds with credit VIX. Economics Letters, 237. https://doi.org/10.1016/j.econlet.2024.111630.
    21. Caldara, D., & Iacoviello, M. (2022). Measuring Geopolitical Risk. American Economic Review, 112(4), 1194–1225. https://doi.org/10.1257/aer.20191823
    22. Calderón, C., & Liu, L. (2003). The direction of causality between financial development and economic growth. Journal of Development Economics, 72(1), 321–334. https://doi.org/10.1016/S0304-3878(03)00079-8.
    23. Cevik, E. I., Caliskan Terzioglu, H., Kilic, Y., Bugan, M. F., & Dibooglu, S. (2024). Interconnectedness and systemic risk: Evidence from global stock markets. Research in International Business and Finance, 69, 102282. https://doi.org/10.1016/j.ribaf.2024.102282.
    24. Chang, S. L., Chien, C. Y., Lee, H. C., & Lin, C. (2018). Historical high and stock index returns: Application of the regression kink model. Journal of International Financial Markets, Institutions and Money, 52, 48–63. https://doi.org/10.1016/j.intfin.2017.08.004.
    25. Chaudhari, K., & Thakkar, A. (2023). Neural network systems with an integrated coefficient of variation-based feature selection for stock price and trend prediction. Expert Systems with Applications, 219. https://doi.org/10.1016/j.eswa.2023.119527.
    26. Chen, W., Liu, W., Zheng, J., & Zhang, X. (2025). Leveraging large language model as news sentiment predictor in stock markets: a knowledge-enhanced strategy. Discover Computing, 28(1), 74. https://doi.org/10.1007/s10791-025-09573-7.
    27. Chowdhury, E. K., & Khan, I. I. (2023). Reactions of Global Stock Markets to the Russia–Ukraine War: An Empirical Evidence. Asia-Pacific Finan-cial Markets. https://doi.org/10.1007/s10690-023-09429-4.
    28. Ciner, C., Gurdgiev, C., & Lucey, B. M. (2013). Hedges and safe havens: An examination of stocks, bonds, gold, oil and exchange rates. Internation-al Review of Financial Analysis, 29. https://doi.org/10.1016/j.irfa.2012.12.001.
    29. Das, N., Sadhukhan, B., Ghosh, R., & Chakrabarti, S. (2024). Developing Hybrid Deep Learning Models for Stock Price Prediction Using Enhanced Twitter Sentiment Score and Technical Indicators. Computational Economics. https://doi.org/10.1007/s10614-024-10566-9.
    30. Dequiedt, V., Gomes, M., Pukthuanthong, K., & Williams-Rambaud, B. (2025). Commodity Dependence and Optimal Asset Allocation. Journal of Futures Markets. https://doi.org/10.1002/fut.22563
    31. Diebold, F. X., & Yilmaz, K. (2014). On the network topology of variance decompositions: Measuring the connectedness of financial firms. Journal of Econometrics, 182(1), 119–134. https://doi.org/10.1016/j.jeconom.2014.04.012.
    32. Diebold, F. X., & Yilmaz, K. (2015). Trans-Atlantic equity volatility connectedness: U.S. and European financial institutions, 2004-2014. Journal of Financial Econometrics, 14(1), 81–127. https://doi.org/10.1093/jjfinec/nbv021.
    33. Doojav, G. O., Purevdorj, M., & Batjargal, A. (2024). The macroeconomic effects of exchange rate movements in a commodity-exporting developing economy. International Economics, 177. https://doi.org/10.1016/j.inteco.2023.100475.
    34. Evrim Mandacı, P., Cagli, E. Ç., & Taşkın, D. (2020). Dynamic connectedness and portfolio strategies: Energy and metal markets. Resources Policy, 68. https://doi.org/10.1016/j.resourpol.2020.101778.
    35. Ghaemi Asl, M., & Ben Jabeur, S. (2024). Could the Russia-Ukraine war stir up the persistent memory of interconnectivity among Islamic equity markets, energy commodities, and environmental factors? Research in International Business and Finance, 69. https://doi.org/10.1016/j.ribaf.2024.102260.
    36. Gülmez, B. (2023). Stock price prediction with optimized deep LSTM network with artificial rabbits optimization algorithm. Expert Systems with Ap-plications, 227. https://doi.org/10.1016/j.eswa.2023.120346.
    37. Hamdi, B., Aloui, M., Alqahtani, F., & Tiwari, A. (2019). Relationship between the oil price volatility and sectoral stock markets in oil-exporting economies: Evidence from wavelet nonlinear denoised based quantile and Granger-causality analysis. Energy Economics, 80, 536–552. https://doi.org/10.1016/j.eneco.2018.12.021.
    38. Hamdi, M., & Aloui, C. (2015). Forecasting crude oil price using artificial neural networks: A literature survey. Economics Bulletin, 35(2).
    39. Hamilton, J. D. (2016). Macroeconomic Regimes and Regime Shifts. In Handbook of Macroeconomics (1st ed., Vol. 2). Elsevier B.V. https://doi.org/10.1016/bs.hesmac.2016.03.004.
    40. Hanif, W., Hadhri, S., & El Khoury, R. (2024). Quantile spillovers and connectedness between oil shocks and stock markets of the largest oil produc-ers and consumers. Journal of Commodity Markets, 34, 100404. https://doi.org/10.1016/j.jcomm.2024.100404.
    41. Hanif, W., Mensi, W., Gubareva, M., & Teplova, T. (2023). Impacts of COVID-19 on dynamic return and volatility spillovers between rare earth met-als and renewable energy stock markets. Resources Policy, 80. https://doi.org/10.1016/j.resourpol.2022.103196.
    42. Hiruta, K., & Senoguchi, J. (2025). Wavelet-CNN for temporal data: Enhancing long-term stock price prediction via multi-resolution wavelet decom-position and CNN-based feature extraction. International Journal of Information Management Data Insights, 5(2), 100360. https://doi.org/10.1016/j.jjimei.2025.100360.
    43. Hudecová, K., & Rajčániová, M. (2023). The impact of geopolitical risk on agricultural commodity prices. Agricultural Economics (Zemědělská Ekonomika), 69(4), 129–139. https://doi.org/10.17221/374/2022-AGRICECON.
    44. Hussain, M., & Rehman, R. U. (2023). Volatility connectedness of GCC stock markets: how global oil price volatility drives volatility spillover in GCC stock markets? Environmental Science and Pollution Research, 30(6), 14212–14222. https://doi.org/10.1007/s11356-022-23114-5.
    45. Kang, W., & Ratti, R. A. (2013). Structural oil price shocks and policy uncertainty. Economic Modelling, 35, 314–319. https://doi.org/10.1016/j.econmod.2013.07.025.
    46. Kehinde, T. O., Chan, F. T. S., & Chung, S. H. (2023). Scientometric review and analysis of recent approaches to stock market forecasting: Two dec-ades survey. In Expert Systems with Applications (Vol. 213). Elsevier Ltd. https://doi.org/10.1016/j.eswa.2022.119299.
    47. Kilic, M., & Wachter, J. A. (2018). Risk, Unemployment, and the Stock Market: A Rare-Event-Based Explanation of Labor Market Volatility. The Review of Financial Studies, 31(12), 4762–4814. https://doi.org/10.1093/rfs/hhy008.
    48. Laopodis, N. T. (2025). Do the Fama–French Factors Proxy Geopolitical Risks? Journal of Portfolio Management, 51, 131–157. https://doi.org/10.3905/jpm.2024.1.656.
    49. Lin, L., Jiang, Y., Xiao, H., & Zhou, Z. (2020). Crude oil price forecasting based on a novel hybrid long memory GARCH-M and wavelet analysis model. Physica A: Statistical Mechanics and Its Applications, 543. https://doi.org/10.1016/j.physa.2019.123532.
    50. Liu, L., Ma, F., Zeng, Q., & Zhang, Y. (2020). Forecasting the aggregate stock market volatility in a data-rich world. Applied Economics, 52(32), 3448–3463. https://doi.org/10.1080/00036846.2020.1713291.
    51. Mensi, W., Maitra, D., Selmi, R., & Vo, X. V. (2023). Extreme dependencies and spillovers between gold and stock markets: evidence from MENA countries. Financial Innovation, 9(1). https://doi.org/10.1186/s40854-023-00451-z.
    52. Mensi, W., Shafiullah, M., Vo, X. V., & Kang, S. H. (2021). Volatility spillovers between strategic commodity futures and stock markets and portfolio implications: Evidence from developed and emerging economies. Resources Policy, 71. https://doi.org/10.1016/j.resourpol.2021.102002.
    53. Mezghani, T., & Abbes, M. B. (2023). Influence of oil price fluctuations on the network connectedness between oil, GCC Islamic and conventional financial markets. International Journal of Islamic and Middle Eastern Finance and Management. https://doi.org/10.1108/IMEFM-09-2021-0392.
    54. Migliorelli, M., & Marini, V. (2020). Sustainability-Related Risks, Risk Management Frameworks and Non-financial Disclosure. https://doi.org/10.1007/978-3-030-54530-7_4.
    55. Nguyen, D. K., Sermpinis, G., & Stasinakis, C. (2023). Big data, artificial intelligence and machine learning: A transformative symbiosis in favour of financial technology. European Financial Management, 29(2), 517–548. https://doi.org/10.1111/eufm.12365.
    56. Nguyen, H. T., & Pham, H. V. D. (2014). Relationship between Stock Market Development and Economic Growth: Evidences from Canada and Australia. International Journal of Economics and Finance, 6(7). https://doi.org/10.5539/ijef.v6n7p1.
    57. Palomba, G., & Tedeschi, M. (2025). Geopolitical Risks’ Spillovers Across Countries and on Commodity Markets: A Dynamic Analysis. Energy RE-SEARCH LETTERS, 6(2). https://doi.org/10.46557/001c.121262.
    58. Paramati, S. R., Safiullah, M., & Soytas, U. (2025). Does geopolitical risk increase carbon emissions and public health risk? Energy Economics, 143. https://doi.org/10.1016/j.eneco.2025.108235.
    59. Pham, D. C., Do, T. N. A., Doan, T. N., Nguyen, T. X. H., & Pham, T. K. Y. (2021). The impact of sustainability practices on financial performance: empirical evidence from Sweden. Cogent Business and Management, 8(1). https://doi.org/10.1080/23311975.2021.1912526.
    60. Qiao, G., Cui, W., Zhou, Y., & Liang, C. (2024). Volatility of Volatility and VIX Forecasting: New Evidence Based on Jumps, the Short-Term and Long-Term Volatility. Journal of Futures Markets. https://doi.org/10.1002/fut.22553.
    61. Rajwani, S., Tiwari, A. K., Yadav, M. P., & Sharma, S. (2023). Dynamic Linkages of Energy Commodities with Bullion and Metal Market: Evidence of Portfolio Hedging. American Business Review, 26(1), 148–179. https://doi.org/10.37625/abr.26.1.148-179.
    62. Rani, M., & Singh, R. (2024). Role of Artificial Intelligence in Sustainable Finance (pp. 409–419). https://doi.org/10.1007/978-3-031-50300-9_22.
    63. Rao, A., Lucey, B., & Kumar, S. (2025). Temporal dynamics of geopolitical risk: An empirical study on energy commodity interest-adjusted spreads. Energy Economics, 141. https://doi.org/10.1016/j.eneco.2024.108066.
    64. Raza, N., Jawad Hussain Shahzad, S., Tiwari, A. K., & Shahbaz, M. (2016). Asymmetric impact of gold, oil prices and their volatilities on stock pric-es of emerging markets. Resources Policy, 49, 290–301. https://doi.org/10.1016/j.resourpol.2016.06.011.
    65. Reber, B. (2014). Estimating the risk–return profile of new venture investments using a risk-neutral framework and ‘thick’ models. The European Journal of Finance, 20(4), 341–360. https://doi.org/10.1080/1351847X.2012.708471.
    66. Rufaidah, F., Karyani, T., Wulandari, E., & Setiawan, I. (2023). A Review of the Implementation of Financial Technology (Fintech) in the Indonesian Agricultural Sector: Issues, Access, and Challenges. In International Journal of Financial Studies (Vol. 11, Issue 3). https://doi.org/10.3390/ijfs11030108.
    67. Sabkha, S., de Peretti, C., & Mallek, S. (2020). Forecasting sovereign CDS VOLATILITY: A comparison of univariate GARCH-class models. Vie & Sciences de l’entreprise, N° 209(1), 27–56. https://doi.org/10.3917/vse.209.0027.
    68. Sagheer, A., & Kotb, M. (2019). Time series forecasting of petroleum production using deep LSTM recurrent networks. Neurocomputing, 323, 203–213. https://doi.org/10.1016/j.neucom.2018.09.082.
    69. Saravanan, N., Olivares-Aguila, J., & Vital-Soto, A. (2022). Bibliometric and Text Analytics Approaches to Review COVID-19 Impacts on Supply Chains. In Sustainability (Switzerland) (Vol. 14, Issue 23). https://doi.org/10.3390/su142315943.
    70. Sharma, A., & Goel, U. (2022). Stock Market Forecasting Using Machine Learning. Global Sci-Tech, 14(1). https://doi.org/10.5958/2455-7110.2022.00003.9
    71. SHEFRIN, H., & STATMAN, M. (1985). The Disposition to Sell Winners Too Early and Ride Losers Too Long: Theory and Evidence. The Journal of Finance, 40(3), 777–790. https://doi.org/10.1111/j.1540-6261.1985.tb05002.x.
    72. Singhal, S., & Ghosh, S. (2016). Returns and volatility linkages between international crude oil price, metal and other stock indices in India: Evidence from VAR-DCC-GARCH models. Resources Policy, 50, 276–288. https://doi.org/10.1016/j.resourpol.2016.10.001.
    73. Soni, R. K., Nandan, T., & Chatnani, N. N. (2023). Dynamic association of economic policy uncertainty with oil, stock and gold: a wavelet-based approach. Journal of Economic Studies, 50(7), 1501–1525. https://doi.org/10.1108/JES-05-2022-0267.
    74. Sun, C., Peng, Y., & Zhan, Y. (2023). How does China’s crude oil futures affect the crude oil prices at home and abroad? Evidence from the cross-market exchange rate spillovers. International Review of Economics and Finance, 88, 204–222. https://doi.org/10.1016/j.iref.2023.06.013.
    75. TETLOCK, P. C. (2007). Giving Content to Investor Sentiment: The Role of Media in the Stock Market. The Journal of Finance, 62(3), 1139–1168. https://doi.org/10.1111/j.1540-6261.2007.01232.x.
    76. Trabelsi, N., Gozgor, G., Tiwari, A. K., & Hammoudeh, S. (2021). Effects of Price of Gold on Bombay Stock Exchange Sectoral Indices: New Evi-dence for Portfolio Risk Management. Research in International Business and Finance, 55. https://doi.org/10.1016/j.ribaf.2020.101316
    77. Wang, M., & Su, Y. (2023). How Russian-Ukrainian geopolitical risks affect Chinese commodity and financial markets? Finance Research Letters, 56. https://doi.org/10.1016/j.frl.2023.104179.
    78. Woode, J. K., Idun, A. A. A., & Kawor, S. (2024). Comovement between agricultural commodities and stock returns of commodity-dependant sub-saharan Africa countries amidst the COVID-19 pandemic. Scientific African, 23. https://doi.org/10.1016/j.sciaf.2023.e01972.
    79. Wu, Y., & Zhang, C. (2022). Hard to arbitrage, hard for analysts to forecast. North American Journal of Economics and Finance, 62. https://doi.org/10.1016/j.najef.2022.101792.
    80. Yang, F., Chen, J., & Liu, Y. (2023). Improved and optimized recurrent neural network based on PSO and its application in stock price prediction. Soft Computing, 27(6), 3461–3476. https://doi.org/10.1007/s00500-021-06113-5.
    81. Yang, J., Zhang, W., Zhang, X., Zhou, J., & Zhang, P. (2023). Enhancing stock movement prediction with market index and curriculum learning. Ex-pert Systems with Applications, 213. https://doi.org/10.1016/j.eswa.2022.118800.
    82. Yun, K. K., Yoon, S. W., & Won, D. (2023). Interpretable stock price forecasting model using genetic algorithm-machine learning regressions and best feature subset selection. Expert Systems with Applications, 213. https://doi.org/10.1016/j.eswa.2022.118803.
    83. Yuni, D. N., Enwo-Irem, I. N., & Urom, C. (2024). Dynamic effects of geopolitical risks and infectious diseases on real estate markets. International Journal of Housing Markets and Analysis, 17, 170–191. https://doi.org/10.1108/IJHMA-09-2022-0130.
    84. Zhang, X., & Zhang, W. (2023). Information asymmetry, sentiment interactions, and asset price. North American Journal of Economics and Finance, 67. https://doi.org/10.1016/j.najef.2023.101920.
    85. Zhu, Z., Sun, L., Tu, J., & Ji, Q. (2022). Oil price shocks and stock market anomalies. Financial Management, 51(2). https://doi.org/10.1111/fima.12377.

  • Downloads

  • How to Cite

    V, V. B., P S, R., & M, S. (2025). Geopolitical Shocks and Commodity Market Dynamics. International Journal of Accounting and Economics Studies, 12(8), 129-140. https://doi.org/10.14419/sj0tbc52