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Forecasting Spot and Futures Price Volatility of Agricultural Commodities: The Role of Climate-Related Migration Uncertainty

Author

Listed:
  • Afees A. Salisu

    (Centre for Econometrics and Applied Research, Ibadan, Nigeria; Department of Economics, University of Pretoria, Pretoria, 0002, South Africa)

  • Ahamuefula E. Ogbonna

    (Centre for Econometrics and Applied Research, Ibadan, Nigeria)

  • Rangan Gupta

    (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa)

  • Elie Bouri

    (School of Business, Lebanese American University, Lebanon)

Abstract

We evaluate the predictive ability of the newly developed climate-related migration uncertainty index (CMUI) and its two components, the climate uncertainty index (CUI) and the migration uncertainty index (MUI), for the return volatility of agricultural commodity prices in both futures and spot markets. Employing a GARCH-MIDAS model, based on mixed data frequencies covering the period from 1977Q4 (with the earliest daily observation on October 3, 1977) to 2024Q1 (with the latest daily observation on March 29, 2024), we conduct both statistical and economic evaluations, including the Modified Diebold-Mariano test, Model Confidence Set procedure, and risk-adjusted performance metrics. The results demonstrate that integrating CUI, MUI, and CMUI into the predictive model of the return volatility of agricultural commodity prices significantly improves forecast accuracy relative to the conventional GARCH-MIDAS-RV benchmark. These findings suggest that the climate and migration related uncertainty indices are both statistically significant and economically relevant, offering enhanced predictive power and investment performance.

Suggested Citation

  • Afees A. Salisu & Ahamuefula E. Ogbonna & Rangan Gupta & Elie Bouri, 2025. "Forecasting Spot and Futures Price Volatility of Agricultural Commodities: The Role of Climate-Related Migration Uncertainty," Working Papers 202516, University of Pretoria, Department of Economics.
    • Handle: RePEc:pre:wpaper:202516
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    References listed on IDEAS

    as
    1. Jiawen Luo & Langnan Chen, 2019. "Multivariate realized volatility forecasts of agricultural commodity futures," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 39(12), pages 1565-1586, December.
    2. Ioannis Chatziantoniou, Stavros Degiannakis, George Filis, and Tim Lloyd, 2021. "Oil price volatility is effective in predicting food price volatility. Or is it?," The Energy Journal, International Association for Energy Economics, vol. 0(Number 6).
    3. Matteo Bonato & Oğuzhan Çepni & Rangan Gupta & Christian Pierdzioch, 2023. "El Niño, La Niña, and forecastability of the realized variance of agricultural commodity prices: Evidence from a machine learning approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(4), pages 785-801, July.
    4. Matteo Bonato & Oguzhan Cepni & Rangan Gupta & Christian Pierdzioch, 2024. "Forecasting the realized volatility of agricultural commodity prices: Does sentiment matter?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(6), pages 2088-2125, September.
    5. Tian, Fengping & Yang, Ke & Chen, Langnan, 2017. "Realized volatility forecasting of agricultural commodity futures using the HAR model with time-varying sparsity," International Journal of Forecasting, Elsevier, vol. 33(1), pages 132-152.
    6. Liu, Jing & Ma, Feng & Tang, Yingkai & Zhang, Yaojie, 2019. "Geopolitical risk and oil volatility: A new insight," Energy Economics, Elsevier, vol. 84(C).
    7. Peter R. Hansen & Asger Lunde & James M. Nason, 2011. "The Model Confidence Set," Econometrica, Econometric Society, vol. 79(2), pages 453-497, March.
    8. Luo, Jiawen & Klein, Tony & Ji, Qiang & Hou, Chenghan, 2019. "Forecasting Realized Volatility of Agricultural Commodity Futures with Infinite Hidden Markov HAR Models," QBS Working Paper Series 2019/10, Queen's University Belfast, Queen's Business School.
    9. Hardik A. Marfatia & Qiang Ji & Jiawen Luo, 2022. "Forecasting the volatility of agricultural commodity futures: The role of co‐volatility and oil volatility," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(2), pages 383-404, March.
    10. Fengping Tian & Ke Yang & Langnan Chen, 2017. "Realized Volatility Forecasting of Agricultural Commodity Futures Using Long Memory and Regime Switching," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 36(4), pages 421-430, July.
    11. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    12. 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.
    13. 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.
    14. Degiannakis, Stavros & Filis, George & Klein, Tony & Walther, Thomas, 2022. "Forecasting realized volatility of agricultural commodities," International Journal of Forecasting, Elsevier, vol. 38(1), pages 74-96.
    15. Ser-Huang Poon & Clive W.J. Granger, 2003. "Forecasting Volatility in Financial Markets: A Review," Journal of Economic Literature, American Economic Association, vol. 41(2), pages 478-539, June.
    16. Jiawen Luo & Qun Zhang, 2024. "Air pollution, weather factors, and realized volatility forecasts of agricultural commodity futures," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 44(2), pages 151-217, February.
    17. Colacito, Riccardo & Engle, Robert F. & Ghysels, Eric, 2011. "A component model for dynamic correlations," Journal of Econometrics, Elsevier, vol. 164(1), pages 45-59, September.
    18. 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.
    19. Sisa Shiba & Goodness C. Aye & Rangan Gupta & Samrat Goswami, 2022. "Forecastability of Agricultural Commodity Futures Realised Volatility with Daily Infectious Disease-Related Uncertainty," JRFM, MDPI, vol. 15(11), pages 1-15, November.
    20. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
    21. Ordu, Beyza Mina & Oran, Adil & Soytas, Ugur, 2018. "Is food financialized? Yes, but only when liquidity is abundant," Journal of Banking & Finance, Elsevier, vol. 95(C), pages 82-96.
    22. Faccini, Renato & Matin, Rastin & Skiadopoulos, George, 2023. "Dissecting climate risks: Are they reflected in stock prices?," Journal of Banking & Finance, Elsevier, vol. 155(C).
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    Keywords

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    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • D8 - Microeconomics - - Information, Knowledge, and Uncertainty
    • F22 - International Economics - - International Factor Movements and International Business - - - International Migration
    • Q02 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General - - - Commodity Market
    • Q13 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Agricultural Markets and Marketing; Cooperatives; Agribusiness

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