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Forecastability of Agricultural Commodity Futures Realised Volatility with Daily Infectious Disease-Related Uncertainty

Author

Listed:
  • Sisa Shiba

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

  • Goodness C. Aye

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

  • Rangan Gupta

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

  • Samrat Goswami

    (Department of Rural Management and Development, Tripura University, Suryamaninagar, 799022, Tripura, India)

Abstract

This paper assesses the predictability of daily infectious diseases-related uncertainty (EMVID) for commodity trading futures in the agricultural bracket, sometimes known as the softs using the heterogeneous autoregressive realised variance (HAR-RV) model. Considering the short-, medium- and long-run recursive out-of-sample estimation approach, we estimate daily realised volatility by using intraday data within the 5-min interval for fifteen agricultural commodity futures. Our results shed a light on the important role EMVID play in predicting the future path of these commodity assets in all time horizons during the COVID-19 episode. These results contain important implications for investors, portfolio managers as well as speculators amid infectious disease-related uncertainty.

Suggested Citation

  • Sisa Shiba & Goodness C. Aye & Rangan Gupta & Samrat Goswami, 2022. "Forecastability of Agricultural Commodity Futures Realised Volatility with Daily Infectious Disease-Related Uncertainty," Working Papers 202249, University of Pretoria, Department of Economics.
    • Handle: RePEc:pre:wpaper:202249
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    Cited by:

    1. Khan, Naveed & Yaya, OlaOluwa S. & Vo, Xuan Vinh & Zada, Hassan, 2025. "Quantile time-frequency connectedness and spillovers among financial stress, cryptocurrencies and commodities," Resources Policy, Elsevier, vol. 103(C).
    2. Rangan Gupta & Christian Pierdzioch, 2024. "Multi-Task Forecasting of the Realized Volatilities of Agricultural Commodity Prices," Mathematics, MDPI, vol. 12(18), pages 1-26, September.
    3. 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.
    4. Salisu, Afees A. & Ogbonna, Ahamuefula E. & Gupta, Rangan & Bouri, Elie, 2025. "Forecasting spot and futures price volatility of agricultural commodities: The role of climate-related migration uncertainty," Research in International Business and Finance, Elsevier, vol. 80(C).
    5. Bonato, Matteo & Cepni, Oguzhan & Gupta, Rangan & Pierdzioch, Christian, 2024. "Financial stress and realized volatility: The case of agricultural commodities," Research in International Business and Finance, Elsevier, vol. 71(C).

    More about this item

    Keywords

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    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • Q02 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General - - - Commodity Market

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