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Comparing the great recession and COVID‐19 using Long Short‐Term Memory: A close look into agricultural commodity prices

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  • Modhurima Dey Amin
  • Syed Badruddoza
  • Oscar Sarasty

Abstract

We employ a neural network (NN) approach—Long Short‐Term Memory (LSTM)—to study agricultural commodity prices during the 2008 Great Recession and the COVID‐19 recession. Our analysis reveals more structural breaks and higher volatility in plant‐based commodities like corn and soybeans during recessions compared with animal‐based commodities. The price reactions varied among commodities, with corn responding first to both recessions, while milk price, which was found independent of other prices, recovered last from the financial recession and first from the disease‐induced recession. This insight into commodity behavior during recessions can aid in trend prediction and recession preparation for investors and researchers.

Suggested Citation

  • Modhurima Dey Amin & Syed Badruddoza & Oscar Sarasty, 2024. "Comparing the great recession and COVID‐19 using Long Short‐Term Memory: A close look into agricultural commodity prices," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 46(4), pages 1406-1428, December.
    • Handle: RePEc:wly:apecpp:v:46:y:2024:i:4:p:1406-1428
    DOI: 10.1002/aepp.13472
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    1. Lawrence, John D. & Mintert, James R. & Anderson, John D. & Anderson, David P., 2008. "Feed Grains and Livestock: Impacts on Meat Supplies and Prices," Choices: The Magazine of Food, Farm, and Resource Issues, Agricultural and Applied Economics Association, vol. 23(2), pages 1-5.
    2. William G. Tomek & Robert J. Myers, 1993. "Empirical Analysis of Agricultural Commodity Prices: A Viewpoint," Review of Agricultural Economics, Agricultural and Applied Economics Association, vol. 15(1), pages 181-202.
    3. Anderson, Kym & Nelgen, Signe, 2012. "Trade Barrier Volatility and Agricultural Price Stabilization," World Development, Elsevier, vol. 40(1), pages 36-48.
    4. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    5. Jushan Bai & Pierre Perron, 1998. "Estimating and Testing Linear Models with Multiple Structural Changes," Econometrica, Econometric Society, vol. 66(1), pages 47-78, January.
    6. Yuanyuan Zhang & Taufiq Choudhry, 2015. "Forecasting the daily dynamic hedge ratios by GARCH models: evidence from the agricultural futures markets," The European Journal of Finance, Taylor & Francis Journals, vol. 21(4), pages 376-399, March.
    7. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    8. Koirala, Krishna H. & Mishra, Ashok K. & D'Antoni, Jeremy M. & Mehlhorn, Joey E., 2015. "Energy prices and agricultural commodity prices: Testing correlation using copulas method," Energy, Elsevier, vol. 81(C), pages 430-436.
    9. Tomek, William G. & Myers, Robert J., 1993. "Empirical Analysis Of Agricultural Commodity Prices: A Viewpoint," Working Papers 6847, Cornell University, Department of Applied Economics and Management.
    10. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    11. Jayson Beckman & Amanda M. Countryman, 2021. "The Importance of Agriculture in the Economy: Impacts from COVID‐19," American Journal of Agricultural Economics, John Wiley & Sons, vol. 103(5), pages 1595-1611, October.
    12. Mensi, Walid & Sensoy, Ahmet & Vo, Xuan Vinh & Kang, Sang Hoon, 2020. "Impact of COVID-19 outbreak on asymmetric multifractality of gold and oil prices," Resources Policy, Elsevier, vol. 69(C).
    13. Walden, Michael L., 2014. "Recovery from the Great Recession: Explaining Differences Among the States," Journal of Regional Analysis and Policy, Mid-Continent Regional Science Association, vol. 44(2).
    14. Maria A. Arias & Yi Wen, 2015. "Recovery from the Great Recession Has Varied around the World," The Regional Economist, Federal Reserve Bank of St. Louis, issue Oct.
    15. Andrews, Donald W K, 1993. "Tests for Parameter Instability and Structural Change with Unknown Change Point," Econometrica, Econometric Society, vol. 61(4), pages 821-856, July.
    16. Hung, Ngo Thai, 2021. "Oil prices and agricultural commodity markets: Evidence from pre and during COVID-19 outbreak," Resources Policy, Elsevier, vol. 73(C).
    17. James O. Bukenya & Natasha R. Wright, 2007. "Determinants of consumer attitudes and purchase intentions with regard to genetically modified tomatoes," Agribusiness, John Wiley & Sons, Ltd., vol. 23(1), pages 117-130.
    18. John M. Abowd & Kevin L. McKinney & Nellie L. Zhao, 2018. "Earnings Inequality and Mobility Trends in the United States: Nationally Representative Estimates from Longitudinally Linked Employer-Employee Data," Journal of Labor Economics, University of Chicago Press, vol. 36(S1), pages 183-300.
    19. Fernandez-Perez, Adrian & Frijns, Bart & Tourani-Rad, Alireza, 2016. "Contemporaneous interactions among fuel, biofuel and agricultural commodities," Energy Economics, Elsevier, vol. 58(C), pages 1-10.
    20. Nazlioglu, Saban & Soytas, Ugur, 2012. "Oil price, agricultural commodity prices, and the dollar: A panel cointegration and causality analysis," Energy Economics, Elsevier, vol. 34(4), pages 1098-1104.
    21. Avalos, Fernando, 2014. "Do oil prices drive food prices? The tale of a structural break," Journal of International Money and Finance, Elsevier, vol. 42(C), pages 253-271.
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