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Commodity Prices after COVID-19: Persistence and Time Trends

Author

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  • Manuel Monge

    (Faculty of Law, Business and Government, Universidad Francisco de Vitoria, E-28223 Madrid, Spain
    Current Address: Department of Financial Economics, Universidad Francisco de Vitoria, Crta. Pozuelo-Majadahonda, Km. 1800, Pozuelo de Alarcón, E-28223 Madrid, Spain.)

  • Ana Lazcano

    (Faculty of Law, Business and Government, Universidad Francisco de Vitoria, E-28223 Madrid, Spain
    Departamento de Ingeniería de Sistemas y Control, Universidad Nacional de Educación a Distancia (UNED), E-28040 Madrid, Spain
    Current Address: Departamento de Ingeniería de Sistemas y Control, ETSI Informática, UNED, C/Juan del Rosal, 16, E-28040 Madrid, Spain.)

Abstract

Since December 2019 we have been living with the virus known as SARS-CoV-2, a situation which has led to health policies being given prevalence over economic ones and has caused a paralysis in the demand for raw materials for several months due to the number confinements put in place around the world. Since the worst days of the pandemic caused by COVID-19, most commodity prices have been recovering. The main objective of this research work is to learn about the evolution and impact of COVID-19 on the prices of raw materials in order to understand how it will affect the behavior of the economy in the coming quarters. To this end, we use fractionally integrated methods and an Artificial Neural Network (ANN) model. During the COVID-19 pandemic episode, we observe that commodity prices have a mean reverting behavior, indicating that it will not be necessary to take additional measures since the series will return, by themselves, to their long term projections. Moreover, in our forecast using ANN algorithms, we observe that the Bloomberg Spot Commodity Index will recover its upward trend, increasing some 56.67% to the price from before the start of the COVID-19 pandemic episode.

Suggested Citation

  • Manuel Monge & Ana Lazcano, 2022. "Commodity Prices after COVID-19: Persistence and Time Trends," Risks, MDPI, vol. 10(6), pages 1-20, June.
    • Handle: RePEc:gam:jrisks:v:10:y:2022:i:6:p:128-:d:840676
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    References listed on IDEAS

    as
    1. Sowell, Fallaw, 1992. "Maximum likelihood estimation of stationary univariate fractionally integrated time series models," Journal of Econometrics, Elsevier, vol. 53(1-3), pages 165-188.
    2. Peter C.B. Phillips, 1987. "Multiple Regression with Integrated Time Series," Cowles Foundation Discussion Papers 852, Cowles Foundation for Research in Economics, Yale University.
    3. So, Mike K.P. & Chu, Amanda M.Y. & Chan, Thomas W.C., 2021. "Impacts of the COVID-19 pandemic on financial market connectedness," Finance Research Letters, Elsevier, vol. 38(C).
    4. Donald Lien & Yiu Kuen Tse, 1999. "Fractional cointegration and futures hedging," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 19(4), pages 457-474, June.
    5. Barkoulas, John T & Labys, Walter C & Onochie, Joseph I, 1999. "Long Memory In Futures Prices," The Financial Review, Eastern Finance Association, vol. 34(1), pages 91-100, February.
    6. Peter C.B. Phillips, 1999. "Discrete Fourier Transforms of Fractional Processes," Cowles Foundation Discussion Papers 1243, Cowles Foundation for Research in Economics, Yale University.
    7. Elliott, Graham & Rothenberg, Thomas J & Stock, James H, 1996. "Efficient Tests for an Autoregressive Unit Root," Econometrica, Econometric Society, vol. 64(4), pages 813-836, July.
    8. Richard, Scott F. & Sundaresan, M., 1981. "A continuous time equilibrium model of forward prices and futures prices in a multigood economy," Journal of Financial Economics, Elsevier, vol. 9(4), pages 347-371, December.
    9. Aslam, Faheem & Aziz, Saqib & Nguyen, Duc Khuong & Mughal, Khurrum S. & Khan, Maaz, 2020. "On the efficiency of foreign exchange markets in times of the COVID-19 pandemic," Technological Forecasting and Social Change, Elsevier, vol. 161(C).
    10. Lutz Kilian, 2009. "Not All Oil Price Shocks Are Alike: Disentangling Demand and Supply Shocks in the Crude Oil Market," American Economic Review, American Economic Association, vol. 99(3), pages 1053-1069, June.
    11. Garbade, Kenneth D & Silber, William L, 1983. "Price Movements and Price Discovery in Futures and Cash Markets," The Review of Economics and Statistics, MIT Press, vol. 65(2), pages 289-297, May.
    12. Neil Kellard & Paul Newbold & Tony Rayner & Christine Ennew, 1999. "The relative efficiency of commodity futures markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 19(4), pages 413-432, June.
    13. Phillips, P C B, 1987. "Time Series Regression with a Unit Root," Econometrica, Econometric Society, vol. 55(2), pages 277-301, March.
    14. Matthew T. Holt & Lee A. Craig, 2006. "Nonlinear Dynamics and Structural Change in the U.S. Hog—Corn Cycle: A Time-Varying STAR Approach," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 88(1), pages 215-233.
    15. Siddhivinayak Kulkarni & Imad Haidar, 2009. "Forecasting Model for Crude Oil Price Using Artificial Neural Networks and Commodity Futures Prices," Papers 0906.4838, arXiv.org.
    16. Robert A. JARROW & George S. OLDFIELD, 2008. "Forward Contracts And Futures Contracts," World Scientific Book Chapters, in: Financial Derivatives Pricing Selected Works of Robert Jarrow, chapter 11, pages 237-246, World Scientific Publishing Co. Pte. Ltd..
    17. Goolsbee, Austan & Syverson, Chad, 2021. "Fear, lockdown, and diversion: Comparing drivers of pandemic economic decline 2020," Journal of Public Economics, Elsevier, vol. 193(C).
    18. Just, Małgorzata & Echaust, Krzysztof, 2020. "Stock market returns, volatility, correlation and liquidity during the COVID-19 crisis: Evidence from the Markov switching approach," Finance Research Letters, Elsevier, vol. 37(C).
    19. Shin, Yongcheol & Schmidt, Peter, 1992. "The KPSS stationarity test as a unit root test," Economics Letters, Elsevier, vol. 38(4), pages 387-392, April.
    20. Sharif, Arshian & Aloui, Chaker & Yarovaya, Larisa, 2020. "COVID-19 pandemic, oil prices, stock market, geopolitical risk and policy uncertainty nexus in the US economy: Fresh evidence from the wavelet-based approach," International Review of Financial Analysis, Elsevier, vol. 70(C).
    21. Luis A. Gil-Alana & Juncal Cunado & Fernando Pérez de Gracia, 2012. "Persistence, Long Memory, and Unit Roots in Commodity Prices," Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, Canadian Agricultural Economics Society/Societe canadienne d'agroeconomie, vol. 60(4), pages 451-468, December.
    22. C. W. J. Granger & Roselyne Joyeux, 1980. "An Introduction To Long‐Memory Time Series Models And Fractional Differencing," Journal of Time Series Analysis, Wiley Blackwell, vol. 1(1), pages 15-29, January.
    23. Erten, Bilge & Ocampo, José Antonio, 2013. "Super Cycles of Commodity Prices Since the Mid-Nineteenth Century," World Development, Elsevier, vol. 44(C), pages 14-30.
    24. Hassler, Uwe & Wolters, Jurgen, 1994. "On the power of unit root tests against fractional alternatives," Economics Letters, Elsevier, vol. 45(1), pages 1-5, May.
    25. Lee, Dongin & Schmidt, Peter, 1996. "On the power of the KPSS test of stationarity against fractionally-integrated alternatives," Journal of Econometrics, Elsevier, vol. 73(1), pages 285-302, July.
    26. Phillips, Peter C.B., 2007. "Unit root log periodogram regression," Journal of Econometrics, Elsevier, vol. 138(1), pages 104-124, May.
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    Cited by:

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    2. Shengwu Du & Travis D. Nesmith & Yanggen Heppe, 2026. "Does Financial Stress Affect Commodity Futures Traders' Positions?," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 46(2), pages 413-434, February.
    3. Matheus Oliveira & Eliseu Monteiro & Abel Rouboa, 2024. "Optimizing Hydrogen-Rich Biofuel Production: Syngas Generation from Wood Chips and Corn Cobs," Energies, MDPI, vol. 17(8), pages 1-13, April.
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    5. Massimiliano Calvia, 2024. "Beef, lamb, pork and poultry meat commodity prices: Historical fluctuations and synchronisation with a focus on recent global crises," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 70(1), pages 24-33.
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    7. Yadav, Miklesh Prasad & Abedin, Mohammad Zoynul & Sinha, Neena & Arya, Vandana, 2024. "Uncovering dynamic connectedness of Artificial intelligence stocks with agri-commodity market in wake of COVID-19 and Russia-Ukraine Invasion," Research in International Business and Finance, Elsevier, vol. 67(PA).
    8. Martin T. Bohl & Niklas Humann & Pierre L. Siklos, 2026. "The Monetary Policy–Commodities Nexus: A Survey," Journal of Economic Surveys, Wiley Blackwell, vol. 40(2), pages 1050-1082, April.

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