Agric. Econ. - Czech, 2026, 72(9):559-570 | DOI: 10.17221/319/2025-AGRICECON
Agricultural commodity markets during the COVID-19 pandemic and the energy crisis: A time-varying copula approachOriginal Paper
- Department of Banking and International Finance, Faculty of Economics and Finance, Bratislava University of Economics and Business, Bratislava, Slovakia
Agricultural commodity markets are highly important not only for investors but also for consumers. Given their importance, closer attention should be paid to agricultural commodity prices and their interactions with other segments of the financial market. In this paper, we apply time-varying copula analysis to investigate the development of the dependence structure between three main groups of agricultural commodities (Bloomberg Grains TR Index, Bloomberg Softs TR Index, and Bloomberg Livestock TR Index) and other segments of the financial market (Brent crude oil, the US Dollar Index, and the broader stock market represented by the S&P 500 stock market index). We use daily data from 2017 to 2024, covering the relatively calm pre-COVID-19 period and the turbulent COVID-19 and subsequent energy crisis periods. The results show that, despite increases in both overall and lower-tail dependence during the pandemic, the relationships remained relatively weak even at their peak. This indicates that the ability of agricultural commodities to provide risk diversification remained intact, even during the two extreme global events. However, the observed time variation indicates that hedging strategies should include dynamic risk monitoring and stress testing.
Keywords: grains, softs, livestock, stock markets, dollar index, risk spillovers
Received: July 30, 2025; Revised: May 11, 2026; Accepted: May 12, 2026; Prepublished online: September 18, 2026; Published: September 30, 2026 Show citation
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References
- Adams Z., Collot S., Kartsakli M. (2020): Have commodities become a financial asset? Evidence from ten years of financialization. Energy Economics, 89: 104769.
Go to original source... - Albulescu C.T., Tiwari A.K., Ji Q. (2020): Copula-based local dependence among energy, agriculture and metal commodities markets. Energy, 202: 117762.
Go to original source... - Aloui R., Hammoudeh S., Nguyen D.K. (2013): A time-varying copula approach to oil and stock market dependence: The case of transition economies. Energy Economics, 39: 208-221.
Go to original source... - Babar M., Ahmad H., Yousaf I. (2024): Returns and volatility spillover between agricultural commodities and emerging stock markets: New evidence from Covid-19 and Russian-Ukrainian war. International Journal of Emerging Markets, 19: 4049-4072.
Go to original source... - Baldi L., Peri M., Vandone D. (2016): Stock markets' bubbles burst and volatility spillovers in agricultural commodity markets. Research in International Business and Finance, 38: 277-285.
Go to original source... - Bollerslev T., Chou R., Kroner K.F. (1992): Arch modeling in finance: A review of the theory and empirical evidence. Journal of Econometrics, 52: 5-59.
Go to original source... - Borgards O., Czudaj R.L., Van Hoang T.H. (2021): Price overreactions in the commodity futures market: An intraday analysis of the Covid-19 pandemic impact. Resources Policy, 71: 101966.
Go to original source...
Go to PubMed... - Brechmann E., Schepsmeier U. (2013): Modeling dependence with C- and D-vine copulas: The R package CDVine. Journal of Statistical Software, 52: 1-27.
Go to original source... - Brunnermeier M.K., Pedersen L.H. (2009): Market liquidity and funding liquidity. The Review of Financial Studies, 22: 2201-2238.
Go to original source... - Brutti Righi M., Ceretta P.S. (2012): Global risk evolution and diversification: A copula-DCC-GARCH model approach. Revista Brasileira de Finanças, 10: 529-550.
Go to original source... - Cabrera B.L., Schulz F. (2016): Volatility linkages between energy and agricultural commodity prices. Energy Economics, 54: 190-203.
Go to original source... - Coval J., Stafford E. (2007): Asset fire sale (and purchases) in equity markets. Journal of Financial Economics, 86: 479-512.
Go to original source... - Cucinotta D., Vanelli M. (2020): WHO declares COVID-19 a pandemic. Acta Biomedica, 91: 157-160.
- Čeryová B., Árendáš P. (2025): Dependence and information flow among U.S. technology industries. Technology Analysis & Strategic Management, 37: 3837-3850.
Go to original source... - Dorosh P., Minot N., Rashid S. (2025): Food price stabilization: Theory and lessons from experience. Food Policy, 137: 102945.
Go to original source... - Engle R. (2002): Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models. Journal of Business & Economic Statistics, 20: 339-350.
Go to original source... - Fousekis P., Grigoriadis V. (2017): Joint price dynamics of quality differentiated commodities: Copula evidence from coffee varieties. European Review of Agricultural Economics, 44: 337-358.
Go to original source... - Genest C., Quessy J.F., Remillard B. (2006): Goodness-of-fit procedures for copula models based on the probability integral transformation. Scandinavian Journal of Statistics, 33: 337-366.
Go to original source... - Giordani P.E., Rocha N., Ruta M. (2016): Food prices and the multiplier effect of trade policy. Journal of International Economics, 101: 102-122.
Go to original source... - Gregoire V., Genest C., Gendron M. (2008): Using copulas to model price dependence in energy markets. Energy Risk, 5: 62-68.
- Han L., Liu H., Zhang W., Wang L. (2023): A comprehensive comparison of copula models and multivariate normal distribution for geo-material parametric data. Computers and Geotechnics, 164: 105777.
Go to original source... - Hansen P., Lunde A. (2001): A forecast comparison of volatility models: Does anything beat a GARCH(1,1)? Journal of Applied Econometrics, 20: 873-889.
Go to original source... - Harri A., Nalley L., Hudson D. (2009): The relationship between oil, exchange rates, and commodity prices. Journal of Agricultural and Applied Economics, 41: 501-510.
Go to original source... - Hung N.T. (2021): Oil prices and agricultural commodity markets: Evidence from pre and during COVID-19 outbreak. Resources Policy, 73: 102236.
Go to original source...
Go to PubMed... - Jana R.K., Ghosh I. (2023): Time-varying relationship between geopolitical uncertainty and agricultural investment. Finance Research Letters, 52: 103521.
Go to original source... - Kim M. (2020): How the financial market can dampen the effects of commodity price shocks. European Economic Review, 121: 103340.
Go to original source... - Letta M., Montalbano P., Pierre G. (2022): Weather shocks, traders' expectations, and food prices. American Journal of Agricultural Economics, 104: 1100-1119.
Go to original source... - Lerkeitthamrong K., Khiewngamdee C., Osathanunkul R. (2019): Impacts of global market volatility and US dollar on agricultural commodity futures prices: A panel cointegration approach. In: Kreinovich V., Sriboonchitta S. (eds): Structural Changes and Their Econometric Modeling. Chiang Mai, Thailand, Jan 9-11, 2019: 412-422.
Go to original source... - Miljkovic D., Vatsa P. (2023): On the linkages between energy and agricultural commodity prices: A dynamic time warping analysis. International Review of Financial Analysis, 90: 102834.
Go to original source... - Nazlioglu S., Soytas U. (2012): Oil price, agricultural commodity prices, and the dollar: A panel cointegration and causality analysis. Energy Economics, 34: 1098-1104.
Go to original source... - Palason K., Rattanasamakarn T., Tansuchat R. (2022): Price volatility dependence structure change among agricultural commodity futures due to extreme event: An analysis with the vine copula. In: Honda K., Entani T., Ubutaka S., Huynh V.N., Inuiguchi M. (eds): 9th International Symposium on Integrated Uncertainty in Knowledge Modelling and Decision Making. Ishikawa, Japan, March 18-19, 2022: 368-378.
Go to original source... - Patton A.J. (2012): A review of copula models for economic time series. Journal of Multivariate Analysis, 110: 4-18.
Go to original source... - Rašiová B., Árendáš P. (2023): Copula approach to market volatility and technology stocks dependence. Finance Research Letters, 52: 103553.
Go to original source... - Rees D.M. (2023): Commodity prices and the US dollar. Journal of International Economics, 157: 104114.
Go to original source... - Rezitis A.N. (2015): The relationship between agricultural commodity prices, crude oil prices and US dollar exchange rates: A panel VAR approach and causality analysis. International Review of Applied Economics, 29: 403-434.
Go to original source... - Sarker R., Roknuzzaman A.S.M., Nazmunnahar, Shahriar M., Hossain M.J., Islam M.R. (2023): The WHO has declared the end of pandemic phase of COVID-19: Way to come back in the normal life. Health Science Reports, 6: e1544.
Go to original source...
Go to PubMed... - Sklar, M. (1959): Fonctions de répartition à n dimensions et leurs marges. Annales de l'ISUP, 8: 229-231. (in French)
- Su C.W., Wang X.Q., Tao R., Oana-Ramona L. (2019): Do oil prices drive agricultural commodity prices? Further evidence in a global bio-energy context. Energy, 172: 691-701.
Go to original source... - Tansuchat R., Rakpho P. (2022): Hedging agriculture commodities futures with histogram data based on conditional copula-GJR-GARCH. In: Honda K., Entani T., Ubutaka S., Huynh V.N., Inuiguchi M. (eds): 9th International Symposium on Integrated Uncertainty in Knowledge Modelling and Decision Making. Ishikawa, Japan, March 18-19, 2022: 305-316. b
Go to original source... - Taskin D., Cagli E.C., Evrim Mandaci P. (2021): The impact of temperature anomalies on commodity futures. Energy Sources, Part B: Economics, Planning, and Policy, 16: 357-370.
Go to original source... - Umar Z., Gubareva M., Teplova T. (2021): The impact of COVID-19 on commodity markets volatility: Analyzing time-frequency relations between commodity prices and coronavirus panic levels. Resources Policy, 73: 102164.
Go to original source...
Go to PubMed... - Wang W., Wells M.T. (2000): Model selection and semiparametric inference for bivariate failure-time data. Journal of the American Statistical Association, 95: 62-72.
Go to original source... - Wang Y., Wu C., Yang L. (2014): Oil price shocks and agricultural commodity prices. Energy Economics, 44: 22-35.
Go to original source... - Zapata H.O., Detre J.D., Hanabuchi T. (2012): Historical performance of commodity and stock markets. Journal of Agricultural and Applied Economics, 44: 339-357.
Go to original source... - Zilberman D., Hochman G., Rajagopal D., Sexton S., Timilsina G. (2013): The impact of biofuels on commodity food prices: Assessment of findings. American Journal of Agricultural Economics, 95: 275-281.
Go to original source...
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