Interdependence and contagion effects in agricultural commodities markets: A bibliometric analysis, implications, and insights for sustainable development
DOI:
https://doi.org/10.24136/eq.2023.029Keywords:
bibliometric analysis, financial econometrics, agricultural commodities interdependence, contagion effectAbstract
Research background: The global interdependence of financial markets due to globalization has resulted in standardized trading conditions for agricultural commodities, reducing the advantages of portfolio diversification. Recent events between 2020 and 2023 underscore the growing importance of real-time information for investors to make informed decisions in this interconnected financial landscape.
Purpose of the article: This article aims to conduct a bibliometric review of the literature about market interdependence. We investigate the contagion effect on agricultural commodities and identify commodities and methods used in the most cited publications from 1997 to 2022.
Methods: A bibliometric analysis was developed, for this, the SCOPUS database was used, sorting with Rayyan, Excel, and finally, the Bibliometrix/R-project to extract bibliometric information from the database.
Findings & value added: The analysis highlights the prominent role of certain countries in contributing to scientific research on this topic, with China and the United States being leaders, collectively producing 24.57% of all publications in the examined periods. The research underscores the global concern for sustainable development, emphasizing the scientific growth linked to this topic and its intersection with energy sources. Unlike other bibliometric studies, this research consolidates relevant methodologies employed in analyzing interdependence and contagion effects in agricultural commodities over the past decades. Additionally, it identifies the most studied commodities in these works. As the world grapples with the challenges of market interdependence, particularly in the wake of recent events between 2020 and 2023, this study underscores the importance of real-time information for informed decision-making. The study suggests a shift towards cleaner and renewable energy sources in the coming years, anticipating increased investments in research and development.
Downloads
References
Ajide, F. M., & Dada, J. T. (2022). The impact of ICT on shadow economy in west Africa. International Social Science Journal, 72(245), 749–767. DOI: https://doi.org/10.1111/issj.12337
View in Google Scholar
Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. DOI: https://doi.org/10.1016/j.joi.2017.08.007
View in Google Scholar
Arnade, C., Cooke, B., & Gale, F. (2017). Agricultural price transmission: China relationships with world commodity markets. Journal of Commodity Markets, 7, 28–40. DOI: https://doi.org/10.1016/j.jcomm.2017.07.001
View in Google Scholar
Balcerzak, A. P., Uddin, G. S., Igliński, B., & Pietrzak, M. B. (2023). Global energy transition: From the main determinants to economic challenges regions. Equilibrium. Quarterly Journal of Economics and Economic Policy, 18(3), 597–608. DOI: https://doi.org/10.24136/eq.2023.018
View in Google Scholar
Baquedano, F. G., & Liefert, W. M. (2014). Market integration and price transmission in consumer markets of developing countries. Food Policy, 44, 103–114. DOI: https://doi.org/10.1016/j.foodpol.2013.11.001
View in Google Scholar
Barbaglia, L., Croux, C., & Wilms, I. (2020). Volatility spillovers in commodity markets: A large t-vector autoregressive approach. Energy Economics, 85, 1–11. DOI: https://doi.org/10.1016/j.eneco.2019.104555
View in Google Scholar
Bashir, U., Zebende, G. F., Yu, Y., Hussain, M., Ali, A., & Abbas, G. (2019). Differential market reactions to pre and post Brexit referendum. Physica A, 515, 151–158. DOI: https://doi.org/10.1016/j.physa.2018.09.182
View in Google Scholar
Beckmann, J., & Czudaj, R. (2014). Volatility transmission in agricultural futures markets. Economic Modelling, 36, 541–546. DOI: https://doi.org/10.1016/j.econmod.2013.09.036
View in Google Scholar
Beckmann, M., & Persson, O. (1998). The thirteen most cited journals in economics. Scientometrics, 42(2), 267–271. DOI: https://doi.org/10.1007/BF02458360
View in Google Scholar
Bertero, E., & Mayer, C. (1990). Structure and performance: Global interdependence of stock markets around the crash of october 1987∗. European Economic Review, 34(6), 1155–1180. DOI: https://doi.org/10.1016/0014-2921(90)90073-8
View in Google Scholar
Beusch, P., Frisk, J. E., Rosén, M., & Dilla, W. (2022). Management control for sustainability: Towards integrated systems. Management Accounting Research, 54, 100777. DOI: https://doi.org/10.1016/j.mar.2021.100777
View in Google Scholar
Bonato, M. (2019). Realized correlations, betas and volatility spillover in the agricultural commodity market: What has changed? Journal of International Financial Markets, Institutions and Money, 62, 184–202. DOI: https://doi.org/10.1016/j.intfin.2019.07.005
View in Google Scholar
Bornmann, L., & Wohlrabe, K. (2019). Normalisation of citation impact in economics. Scientometrics, 120(2), 841–884. DOI: https://doi.org/10.1007/s11192-019-03140-w
View in Google Scholar
Bouri, E., Lucey, B., Saeed, T., & Vo, X. V. (2021). The realized volatility of commodity futures: Interconnectedness and determinants. International Review of Economics & Finance, 73, 139–151. DOI: https://doi.org/10.1016/j.iref.2021.01.006
View in Google Scholar
Bürgi, C., & Wohlrabe, K. (2022). The influence of covid-19 on publications in economics: Bibliometric evidence from five working paper series. Scientometrics, 127(9), 5175–5189. DOI: https://doi.org/10.1007/s11192-022-04473-9
View in Google Scholar
Dahl, R. E., Oglend, A., & Yahya, M. (2020). Dynamics of volatility spillover in commodity markets: Linking crude oil to agriculture. Journal of Commodity Markets, 20, 100111. DOI: https://doi.org/10.1016/j.jcomm.2019.100111
View in Google Scholar
Despard, M., Chun, Y., Grinstein-Weiss, M., & Roll, S. (2020). COVID-19 job and income loss leading to more hunger and financial hardship. Brookings. Retrieved from https://www.brookings.edu/articles/covid-19-job-and-income-loss-leading-to-more-hunger-and-financial-hardship/.
View in Google Scholar
Dias, R., Heliodoro, P., Alexandre, P., Santos, H., & Farinha, A. (2021). Long memory in stock returns: Evidence from the Eastern European markets. SHS Web of Conferences, 91, 01029. DOI: https://doi.org/10.1051/shsconf/20219101029
View in Google Scholar
Diebold, F. X., & Yilmaz, K. (2009). Measuring financial asset return and volatility spillovers, with application to global equity markets. Economic Journal, 119(534), 158–171.
View in Google Scholar
Diebold, F. X., & Yilmaz, K. (2009). Measuring financial asset return and volatility spillovers, with application to global equity markets. Economic Journal, 119(534), 158–171. DOI: https://doi.org/10.1111/j.1468-0297.2008.02208.x
View in Google Scholar
Du, X., Yu, C. L., & Hayes, D. J. (2011). Speculation and volatility spillover in the crude oil and agricultural commodity markets: A Bayesian analysis. Energy Economics, 33(3), 497–503. DOI: https://doi.org/10.1016/j.eneco.2010.12.015
View in Google Scholar
Elsevier (2022). SCOPUS: Expertly curated abstract & citation database. Expertly Curated Abstract & Citation Database. Retrieved from https://www.elsevier. com/solutions/scopus.
View in Google Scholar
Fiszeder, P., & Małecka, M. (2022). Forecasting volatility during the outbreak of Russian invasion of Ukraine: Application to commodities, stock indices, currencies, and cryptocurrencies. Equilibrium. Quarterly Journal of Economics and Economic Policy, 17(4), 939–967. DOI: https://doi.org/10.24136/eq.2022.032
View in Google Scholar
Forbes, K., & Rigobon, R. (2001). Measuring contagion: Conceptual and empirical issues. In International financial contagion (pp. 43–66). Boston: Springer US. DOI: https://doi.org/10.1007/978-1-4757-3314-3_3
View in Google Scholar
Galindo-Rueda, F., & López-Bassols, V. (2022). Implementing the OECD frascati manual: Proposed reference items for business R&D surveys. OECD Science, Technology and Industry Working Papers.
View in Google Scholar
Gasparatos, A., Mudombi, S., Balde, B. S., Von Maltitz, G. P., Johnson, F. X., Romeu-Dalmau, C., Jumbe, C., Ochieng, C., Luhanga, D., Nyambane, A., Rossignoli, C.,Jarzebski, M., Dam Lam, R., Dompreh, E., & Willis, K. J. (2022). Local food security impacts of biofuel crop production in southern Africa. Renewable and Sustainable Energy Reviews, 154, 111875. DOI: https://doi.org/10.1016/j.rser.2021.111875
View in Google Scholar
Grass, I., Loos, J., Baensch, S., Batáry, P., Librán-Embid, F., Ficiciyan, A., Klaus, F., Riechers, M., Rosa, J., Tiede, J., Udy, K., Westphal, C., Wurz, A., & Tscharntke, T. (2019). People and nature. Land-Sharing/-Sparing Connectivity Landscapes for Ecosystem Services and Biodiversity Conservation, 1(2), 262–272. DOI: https://doi.org/10.1002/pan3.21
View in Google Scholar
Guedes, E. F., Ferreira, P., Dionísio, A., & Zebende, G. F. (2019). An econophysics approach to study the effect of BREXIT referendum on European Union stock markets. Physica A, 523, 1175–1182. DOI: https://doi.org/10.1016/j.physa.2019.04.132
View in Google Scholar
Haldar, A., Sucharita, S., Dash, D. P., Sethi, N., & Chandra Padhan, P. (2023). The effects of ICT, electricity consumption, innovation and renewable power generation on economic growth: An income level analysis for the emerging economies. Journal of Cleaner Production, 384, 135607. DOI: https://doi.org/10.1016/j.jclepro.2022.135607
View in Google Scholar
Hamulczuk, M., & Pawlak, K. (2022). Determinants for international competitiveness of the food industry in 43 countries world-wide: evidence from panel models. Equilibrium. Quarterly Journal of Economics and Economic Policy, 17(3), 635–667. DOI: https://doi.org/10.24136/eq.2022.022
View in Google Scholar
Hansen, P. R., Lunde, A., & Voev, V. (2014). Realized beata GARCH: A multivariate GARCH model with realized measures of volatility. Journal of Applied Econometrics, 29(5), 774–799. DOI: https://doi.org/10.1002/jae.2389
View in Google Scholar
Hernandez, J. A., Shahzad, S. J. H., Uddin, G. S., & Kang, S. H. (2019). Can agricultural and precious metal commodities diversify and hedge extreme downside and upside oil market risk? An extreme quantile approach. Resources Policy, 62, 588–601. DOI: https://doi.org/10.1016/j.resourpol.2018.11.007
View in Google Scholar
Hernandez, M. A., Ibarra, R., & Trupkin, D. R. (2014). How far do shocks move across borders? Examining volatility transmission in major agricultural futures markets. European Review of Agricultural Economics, 41(2), 301–325. DOI: https://doi.org/10.1093/erae/jbt020
View in Google Scholar
Herwartz, H., & Saucedo, A. (2020). Food–oil volatility spillovers and the impact of distinct biofuel policies on price uncertainties on feedstock markets. Agricultural Economics, 51(3), 387–402. DOI: https://doi.org/10.1111/agec.12561
View in Google Scholar
Huang, J., Rozelle, S., & Chang, M. (2004). Tracking distortions in agriculture: China and its accession to the World Trade Organization. World Bank Economic Review, 18(1), 59–84. DOI: https://doi.org/10.1093/wber/lhh033
View in Google Scholar
Ji, Q., Bouri, E., Roubaud, D., & Kristoufek, L. (2019). Information interdependence among energy, cryptocurrency and major commodity markets. Energy Economics, 81, 1042–1055. DOI: https://doi.org/10.1016/j.eneco.2019.06.005
View in Google Scholar
Ji, Q., Bouri, E., Roubaud, D., & Shahzad, S. J. H. (2018). Risk spillover between energy and agricultural commodity markets: A dependence-switching CoVaR-copula model. Energy Economics, 75, 14–27. DOI: https://doi.org/10.1016/j.eneco.2018.08.015
View in Google Scholar
Kang, S. H., McIver, R., & Yoon, S.-M. (2017). Dynamic spillover effects among crude oil, precious metal, and agricultural commodity futures markets. Energy Economics, 62, 19–32. DOI: https://doi.org/10.1016/j.eneco.2016.12.011
View in Google Scholar
Kramarova, K., Švábová, L., & Gabrikova, B. (2022). Impacts of the covid-19 crisis on unemployment in Slovakia: A statistically created counterfactual approach using the time series analysis. Equilibrium. Quarterly Journal of Economics and Economic Policy, 17(2), 343–389. DOI: https://doi.org/10.24136/eq.2022.012
View in Google Scholar
Lajeunesse, M. J. (2016). Facilitating systematic reviews, data extraction and meta-analysis with the metagear package for R. Methods in Ecology and Evolution, 7(3), 323–330. DOI: https://doi.org/10.1111/2041-210X.12472
View in Google Scholar
Linnenluecke, M. K., Marrone, M., & Singh, A. K. (2020). Conducting systematic literature reviews and bibliometric analyses. Australian Journal of Management, 45(2), 175–194. DOI: https://doi.org/10.1177/0312896219877678
View in Google Scholar
Liu, N., Xu, Z., & Skare, M. (2021). The research on COVID-19 and economy from 2019 to 2020: Analysis from the perspective of bibliometrics. Oeconomia Copernicana, 12(2), 217–268. DOI: https://doi.org/10.24136/oc.2021.009
View in Google Scholar
Luo, J., & Ji, Q. (2018). High-frequency volatility connectedness between the US crude oil market and China’s agricultural commodity markets. Energy Economics, 76, 424–438. DOI: https://doi.org/10.1016/j.eneco.2018.10.031
View in Google Scholar
Mantegna, R., & Stanley, E. (1999). Introduction to econophysics: Correlations and complexity in finance. Cambridge: Cambridge University Press. DOI: https://doi.org/10.1017/CBO9780511755767
View in Google Scholar
Mensi, W., Hammoudeh, S., Nguyen, D. K., & Yoon, S.-M. (2014). Dynamic spillovers among major energy and cereal commodity prices. Energy Economics, 43, 225–243. DOI: https://doi.org/10.1016/j.eneco.2014.03.004
View in Google Scholar
Mlambo-Thata, B. (2010). Evaluating electronic resource programmes and provision: Case studies from Africa and Asia. Learned Publishing, 23(3), 266–267. DOI: https://doi.org/10.1087/20100311
View in Google Scholar
Nazlioglu, S., Erdem, C., & Soytas, U. (2013). Volatility spillover between oil and agricultural commodity markets. Energy Economics, 36, 658–665. DOI: https://doi.org/10.1016/j.eneco.2012.11.009
View in Google Scholar
Ortiz-Martínez, E., Marín-Hernández, S., & Santos-Jaén, J.-M. (2023). Sustainability, corporate social responsibility, non-financial reporting and company performance: Relationships and mediating effects in Spanish small and medium sized enterprises. Sustainable Production and Consumption, 35, 349–364. DOI: https://doi.org/10.1016/j.spc.2022.11.015
View in Google Scholar
Parra Paitan, C., & Verburg, P. (2019). Methods to assess the impacts and indirect land use change caused by telecoupled agricultural supply chains: A review. Sustainability, 11(4), 1162–1162. DOI: https://doi.org/10.3390/su11041162
View in Google Scholar
Pietrzak, M. B., Fałdziński, M., Balcerzak, A. P., Meluzín, T., & Zinecker, M. (2017). Short-term shocks and long-term relationships of interdependencies among central european capital markets. Economics & Sociology, 10(1), 61–77. DOI: https://doi.org/10.14254/2071-789X.2017/10-1/5
View in Google Scholar
Pimentel, D., Marklein, A., Toth, M. A., Karpoff, M. N., Paul, G. S., McCormack, R., Kyriazis, J., & Krueger, T. (2009). Food Versus biofuels: Environmental and economic costs. Human Ecology, 37(1), 1–12. DOI: https://doi.org/10.1007/s10745-009-9215-8
View in Google Scholar
Prisma-Scr (2022). Transparent reporting of systematic reviews and meta-analyses. Retrieved from https://www.prisma-statement.org/.
View in Google Scholar
Quintino, D. D., Cantarinha, A., & Ferreira, P. J. S. (2021). Relationship between US and Brazilian ethanol prices: New evidence based on fractal regressions. Biofuels, Bioproducts and Biorefining, 15(5), 1215–1220. DOI: https://doi.org/10.1002/bbb.2192
View in Google Scholar
Reboredo, J. C., Rivera-Castro, M. A., & Zebende, G. F. (2014). Oil and US dollar exchange rate dependence: A detrended cross-correlation approach. Energy Economics, 42, 132–139. DOI: https://doi.org/10.1016/j.eneco.2013.12.008
View in Google Scholar
Reboredo, J. C. (2012). Do food and oil prices co-move? Energy Policy, 49, 456–467. DOI: https://doi.org/10.1016/j.enpol.2012.06.035
View in Google Scholar
Rosales-Calderon, O., & Arantes, V. (2019). A review on commercial-scale high-value products that can be produced alongside cellulosic ethanol. Biotechnology for Biofuels, 12(1), 240–240. DOI: https://doi.org/10.1186/s13068-019-1529-1
View in Google Scholar
Rusydiana, A. S., Sukmana, R., Laila, N., & Bahri, M. S. (2022). The nexus between a green economy and islamic finance: Insights from a bibliometric analysis. ICR Journal, 13, 51–71. DOI: https://doi.org/10.52282/icr.v13i1.908
View in Google Scholar
Saghaian, S., Nemati, M., Walters, C. (2018). Asymmetric Price Volatility Transmission between U.S. Biofuel, Corn, and Oil Markets. Journal of Agricultural and Resource Economics, 43(1), 46–60.
View in Google Scholar
Sampaio, C., Farinha, L., Sebastião, J. R., & Régio, M. (2022). How the 2008–2009 financial crisis shaped fair value accounting literature: A bibliometric approach. Administrative Sciences, 12(1), 15–15. DOI: https://doi.org/10.3390/admsci12010015
View in Google Scholar
Santana, M. M. M., Mariano-Neto, E., de Vasconcelos, R. N., Dodonov, P., & Medeiros, J. M. M. (2021). Mapping the research history, collaborations and trends of remote sensing in fire ecology. Scientometrics, 126(2), 1359–1388. DOI: https://doi.org/10.1007/s11192-020-03805-x
View in Google Scholar
Santana, T. P., Horta, N., Revez, C., Dias, R. M. T. S., & Zebende, G. F. (2023). Effects of interdependence and contagion on crude oil and precious metals according to ρDCCA: A COVID-19 case study. Sustainability, 15(5), 3945–3945. DOI: https://doi.org/10.3390/su15053945
View in Google Scholar
Shahzad, S. J. H., Hernandez, J. A., Al-Yahyaee, K. H., & Jammazi, R. (2018). Asymmetric risk spillovers between oil and agricultural commodities. Energy Policy, 118, 182–198. DOI: https://doi.org/10.1016/j.enpol.2018.03.074
View in Google Scholar
Silva, M. F. D., Pereira, É. J. D. A. L., Filho, A. M. D. S., Castro, A. P. N. D., Miranda, J. G. V., & Zebende, G. F. (2015). Quantifying cross-correlation between ibovespa and brazilian blue-chips: The DCCA approach. Physica A: Statistical Mechanics and Its Applications, 424, 124–129. DOI: https://doi.org/10.1016/j.physa.2015.01.002
View in Google Scholar
Silva, M. F. D., Pereira, É. J. D. A. L., Filho, A. M. D. S., Castro, A. P. N. D., Miranda, J. G. V., & Zebende, G. F. (2016). Quantifying the contagion effect of the 2008 financial crisis between the G7 countries (by GDP nominal). Physica A: Statistical Mechanics and Its Applications, 453, 1–8. DOI: https://doi.org/10.1016/j.physa.2016.01.099
View in Google Scholar
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. DOI: https://doi.org/10.1016/j.energy.2019.02.028
View in Google Scholar
Svabova, L., Tesarova, E. N., Durica, M., & Strakova, L. (2021). Evaluation of the impacts of the COVID-19 pandemic on the development of the unemployment rate in Slovakia: Counterfactual before-after comparison. Equilibrium. Quarterly Journal of Economics and Economic Policy, 16(2), 261–284. DOI: https://doi.org/10.24136/eq.2021.010
View in Google Scholar
Tahir, Z., & Riaz, K. (1997). Integration of agricultural commodity markets in Punjab. Pakistan Development Review, 36(3), 241–262. DOI: https://doi.org/10.30541/v36i3pp.241-262
View in Google Scholar
Tiwari, A. K., Abakah, E. J. A., Adewuyi, A. O., & Lee, C.-C. (2022). Quantile risk spillovers between energy and agricultural commodity markets: Evidence from pre and during COVID-19 outbreak. Energy Economics, 113, 106235. DOI: https://doi.org/10.1016/j.eneco.2022.106235
View in Google Scholar
Tokarchuk, D., Pryshliak, N., Shynkovych, A., & Berezyuk, S. (2022). Food security and biofuel production: Solving the dilemma on the example of Ukraine. Polityka Energetyczna – Energy Policy Journal, 25(2), 179–196. DOI: https://doi.org/10.33223/epj/150496
View in Google Scholar
Umar, Z., Gubareva, M., Naeem, M., & Akhter, A. (2021). Return and volatility transmission between oil price shocks and agricultural commodities. PLOS ONE, 16(2), e0246886. DOI: https://doi.org/10.1371/journal.pone.0246886
View in Google Scholar
United Nations (2023). What is Sustainable Development? Retrieved from https://www.un.org/sustainabledevelopment/blog/2023/08/what-is-sustainable-development/.
View in Google Scholar
Vasconcelos, R. N., Lima, A. T. C., Lentini, C. A. D., Miranda, G. V., Mendonça, L. F., Silva, M. A., Cambuí, E., Lopes, J., Porsani, M. J. (2020). Oil spill detection and mapping: A 50-year bibliometric analysis. Remote Sensing, 12(21), 1–18. DOI: https://doi.org/10.3390/rs12213647
View in Google Scholar
Yip, P. S., Brooks, R., Do, H. X., & Nguyen, D. K. (2020). Dynamic volatility spillover effects between oil and agricultural products. International Review of Financial Analysis, 69, 101465. DOI: https://doi.org/10.1016/j.irfa.2020.101465
View in Google Scholar
Zafeiriou, E., Arabatzis, G., Karanikola, P., Tampakis, S., & Tsiantikoudis, S. (2018). Agricultural commodity and crude oil prices: An empirical investigation of their relationship. Sustainability, 10(4), 1199. DOI: https://doi.org/10.3390/su10041199
View in Google Scholar
Downloads
Published
Issue
Section
License
Copyright (c) 2023 Equilibrium. Quarterly Journal of Economics and Economic Policy
This work is licensed under a Creative Commons Attribution 4.0 International License.