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The Roles of Global Supply Chain Pressure and Economic Conditions in Forecasting the VaR of Commodity Markets: A Quantile GARCH-MIDAS Approach

Author

Listed:
  • Zhangying Li

    (Economics and Management School, Wuhan University)

  • O-Chia Chuang

    (School of Digital Economics, Hubei University of Economics)

  • Rangan Gupta

    (Department of Economics, University of Pretoria)

  • Elie Bouri

    (School of Business, Lebanese American University, Lebanon)

Abstract

Accurately predicting the Value-at-Risk (VaR) in commodity markets is crucial for risk management, yet the volatility and cyclicality of commodity prices pose significant challenges. This paper innovatively incorporates the information content of the Global Supply Chain Pressure Index (GSCPI) and the Global Economic Conditions Index (GECON) into the quantile Genaralized Autoregressive Conditional Heteroskedasticty-Mixed Data Sampling (GARCH-MIDAS) framework to address the issue of mismatched data frequencies, and explores the impact of these monthly indicators on daily commodity returns volatility. We find that the MIDAS framework significantly outperforms the conditional autoregressive VaR by regression quantiles (CAViaR) model, with asymmetric models showing superior performance. Both GSCPI and GECON exhibit strong explanatory power for VaR forecasting, highlighting the important influence of global supply and demand conditions on returns volatility of the overall commodity market, as well as its various sub-sectors.

Suggested Citation

  • Zhangying Li & O-Chia Chuang & Rangan Gupta & Elie Bouri, 2025. "The Roles of Global Supply Chain Pressure and Economic Conditions in Forecasting the VaR of Commodity Markets: A Quantile GARCH-MIDAS Approach," Working Papers 202528, University of Pretoria, Department of Economics.
    • Handle: RePEc:pre:wpaper:202528
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    References listed on IDEAS

    as
    1. Afees A. Salisu & Rangan Gupta & Elie Bouri & Qiang Ji, 2022. "Mixed‐frequency forecasting of crude oil volatility based on the information content of global economic conditions," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(1), pages 134-157, January.
    2. Ing-Haw Cheng & Wei Xiong, 2014. "Financialization of Commodity Markets," Annual Review of Financial Economics, Annual Reviews, vol. 6(1), pages 419-441, December.
    3. Ke Tang & Wei Xiong, 2012. "Index Investment and the Financialization of Commodities," Financial Analysts Journal, Taylor & Francis Journals, vol. 68(6), pages 54-74, November.
    4. Wang, Jiqian & Ma, Feng & Bouri, Elie & Zhong, Juandan, 2022. "Volatility of clean energy and natural gas, uncertainty indices, and global economic conditions," Energy Economics, Elsevier, vol. 108(C).
    5. Bouri, Elie & Cepni, Oguzhan & Gupta, Rangan & Liu, Ruipeng, 2025. "Supply chain constraints and the predictability of the conditional distribution of international stock market returns and volatility," Economics Letters, Elsevier, vol. 247(C).
    6. William Ginn & Jamel Saadaoui, 2025. "Do Supply Chain Disruptions Matter for Global Economic Conditions?," The World Economy, Wiley Blackwell, vol. 48(7), pages 1534-1551, July.
    7. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    8. Demirer, Riza & Gupta, Rangan & Suleman, Tahir & Wohar, Mark E., 2018. "Time-varying rare disaster risks, oil returns and volatility," Energy Economics, Elsevier, vol. 75(C), pages 239-248.
    9. Ohashi, Kazuhiko & Okimoto, Tatsuyoshi, 2016. "Increasing trends in the excess comovement of commodity prices," Journal of Commodity Markets, Elsevier, vol. 1(1), pages 48-64.
    10. Ascari, Guido & Bonam, Dennis & Smadu, Andra, 2024. "Global supply chain pressures, inflation, and implications for monetary policy," Journal of International Money and Finance, Elsevier, vol. 142(C).
    11. Turan G. Bali, 2003. "An Extreme Value Approach to Estimating Volatility and Value at Risk," The Journal of Business, University of Chicago Press, vol. 76(1), pages 83-108, January.
    12. Francois Gourio, 2012. "Disaster Risk and Business Cycles," American Economic Review, American Economic Association, vol. 102(6), pages 2734-2766, October.
    13. Christiane Baumeister & Dimitris Korobilis & Thomas K. Lee, 2022. "Energy Markets and Global Economic Conditions," The Review of Economics and Statistics, MIT Press, vol. 104(4), pages 828-844, October.
    14. Gozgor, Giray & Khalfaoui, Rabeh & Yarovaya, Larisa, 2023. "Global supply chain pressure and commodity markets: Evidence from multiple wavelet and quantile connectedness analyses," Finance Research Letters, Elsevier, vol. 54(C).
    15. Laporta, Alessandro G. & Merlo, Luca & Petrella, Lea, 2018. "Selection of Value at Risk Models for Energy Commodities," Energy Economics, Elsevier, vol. 74(C), pages 628-643.
    16. Ing-Haw Cheng & Andrei Kirilenko & Wei Xiong, 2015. "Convective Risk Flows in Commodity Futures Markets," Review of Finance, European Finance Association, vol. 19(5), pages 1733-1781.
    17. Pablo Burriel & Iván Kataryniuk & Carlos Moreno Pérez & Francesca Viani, 2024. "A New Supply Bottlenecks Index Based on Newspaper Data," International Journal of Central Banking, International Journal of Central Banking, vol. 20(2), pages 17-67, April.
    18. Diaz, Elena Maria & Cunado, Juncal & de Gracia, Fernando Perez, 2023. "Commodity price shocks, supply chain disruptions and U.S. inflation," Finance Research Letters, Elsevier, vol. 58(PC).
    19. James D. Hamilton & Jing Cynthia Wu, 2015. "Effects Of Index‐Fund Investing On Commodity Futures Prices," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 56(1), pages 187-205, February.
    20. Silvennoinen, Annastiina & Thorp, Susan, 2013. "Financialization, crisis and commodity correlation dynamics," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 24(C), pages 42-65.
    21. Lux, Thomas & Segnon, Mawuli & Gupta, Rangan, 2016. "Forecasting crude oil price volatility and value-at-risk: Evidence from historical and recent data," Energy Economics, Elsevier, vol. 56(C), pages 117-133.
    22. Buchinsky, Moshe, 1995. "Estimating the asymptotic covariance matrix for quantile regression models a Monte Carlo study," Journal of Econometrics, Elsevier, vol. 68(2), pages 303-338, August.
    23. Xavier Gabaix, 2012. "Variable Rare Disasters: An Exactly Solved Framework for Ten Puzzles in Macro-Finance," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 127(2), pages 645-700.
    24. Qunzi Zhang, 2021. "One hundred years of rare disaster concerns and commodity prices," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(12), pages 1891-1915, December.
    25. Adams, Zeno & Glueck, Thorsten, 2014. "Financialization in Commodity Markets: A Passing Trend or the New Normal?," Working Papers on Finance 1413, University of St. Gallen, School of Finance, revised Aug 2015.
    26. Hahn, Jinyong, 1995. "Bootstrapping Quantile Regression Estimators," Econometric Theory, Cambridge University Press, vol. 11(1), pages 105-121, February.
    27. Robert F. Engle & Simone Manganelli, 2004. "CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles," Journal of Business & Economic Statistics, American Statistical Association, vol. 22, pages 367-381, October.
    28. Adams, Zeno & Glück, Thorsten, 2015. "Financialization in commodity markets: A passing trend or the new normal?," Journal of Banking & Finance, Elsevier, vol. 60(C), pages 93-111.
    29. Robert F. Engle & Eric Ghysels & Bumjean Sohn, 2013. "Stock Market Volatility and Macroeconomic Fundamentals," The Review of Economics and Statistics, MIT Press, vol. 95(3), pages 776-797, July.
    30. Ginn, William, 2024. "Global supply chain disruptions and financial conditions," Economics Letters, Elsevier, vol. 239(C).
    31. Xu, Yan & Wang, Xinyu & Liu, Hening, 2021. "Quantile-based GARCH-MIDAS: Estimating value-at-risk using mixed-frequency information," Finance Research Letters, Elsevier, vol. 43(C).
    32. Jacobus Nel & Rangan Gupta & Mark E. Wohar & Christian Pierdzioch, 2024. "Climate Risks And Predictability Of Commodity Returns And Volatility: Evidence From Over 750 Years Of Data," Climate Change Economics (CCE), World Scientific Publishing Co. Pte. Ltd., vol. 15(04), pages 1-40, November.
    33. James D. Hamilton & Jing Cynthia Wu, 2015. "Effects Of Index‐Fund Investing On Commodity Futures Prices," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 56, pages 187-205, February.
    34. Zakoian, Jean-Michel, 1994. "Threshold heteroskedastic models," Journal of Economic Dynamics and Control, Elsevier, vol. 18(5), pages 931-955, September.
    35. Polat, Onur & Somani, Dhanashree & Gupta, Rangan & Karmakar, Sayar, 2025. "Shortages and machine-learning forecasting of oil returns volatility: 1900–2024," Finance Research Letters, Elsevier, vol. 79(C).
    36. Conrad, Christian & Loch, Karin & Rittler, Daniel, 2014. "On the macroeconomic determinants of long-term volatilities and correlations in U.S. stock and crude oil markets," Journal of Empirical Finance, Elsevier, vol. 29(C), pages 26-40.
    37. Clements, Michael P & Galvão, Ana Beatriz, 2008. "Macroeconomic Forecasting With Mixed-Frequency Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 546-554.
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    Keywords

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    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E23 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Production
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • Q02 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General - - - Commodity Market

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