Information flow in brazilian food market: The influence of the 2008 financial crisis

Authors

DOI:

https://doi.org/10.33448/rsd-v10i7.15978

Keywords:

Agricultural commodities; Transfer entropy; Food crisis.

Abstract

Brazilian agribusiness is gaining more and more international prominence thanks to the relevant production and export of agricultural commodities, as well as the great potential for development that this economic sector has in the country. The subject has attracted the attention of several researchers because, as the export sector grows, prices of these commodities for domestic consumption tend to rise, affecting the price of other foods. Understanding the dynamics of the price relation between these products is, therefore, of great value in order to avoid that possible financial crises also lead to food crises such as that of 2008. In order to contribute in this sense, the prices behavior of soybean, sugar, coffee and live cattle were analyzed here, as they are agricultural commodities produced on a large scale and traded worldwide. The study was based on the prices registered between 01/1997 and 12/2019, at CEPEA/ESALQ/USP, considering the periods before (07/1997 - 06/2007) and after (07/2007 - 12/2019) the 2008 crisis. The technique of analysis used was the Transfer Entropy - TE, that serves to quantify the flow of information between pairs of time series, as well as to obtain the direction of that flow. The technique was then applied to each pair of time series considering the pre-crisis and post-crisis periods. Results showed that the crisis affected the direction of information transfer between the pairs sugar-cattle, sugar-soybean and cattle-soybean. The time series prices of coffee, in turn, transmitted information to the other commodities in both periods.

References

Alan Bojanic, H. (2017). The rapid agricultural development of Brazil in the last 20 years.

Beckmann, J., & Czudaj, R. (2014). Volatility transmission in agricultural futures markets. Economic Modelling, 36, 541-546.

dos Santos Caliari, S. C. (2019). A EXPORTAÇÃO DE CARNE BOVINA NO BRASIL. CIMATech, 1(6), 281-292.

Farias, D. B. C. ., Silva, A. S. A. da ., Stosic, T., & Stosic, B. (2020). Análise de entropia multiescala da dinâmica de preços de commodities agrícolas brasileiras. Research, Society and Development, 9(11), e4739119832.

Gao, H. L., & Mei, D. C. (2019). The correlation structure in the international stock markets during global financial crisis. Physica A: Statistical Mechanics and its Applications, 534, 122056.

He, J., & Shang, P. (2017). Comparison of transfer entropy methods for financial time series. Physica A: Statistical Mechanics and its Applications, 482, 772-785.

Ivanic, M., Martin, W., & Zaman, H. (2012). Estimating the Short-Run Poverty Impacts of the 2010–11 Surge in Food Prices. World Development, 40(11), 2302–2317.

Jale, J. S., Júnior, S. F., Stošić, T., Stošić, B., & Ferreira, T. A. (2019). Information flow between Ibovespa and constituent companies. Physica A: Statistical Mechanics and its Applications, 516, 233-239.

Kristoufek, L., Janda, K., & Zilberman, D. (2012). Correlations between biofuels and related commodities before and during the food crisis: A taxonomy perspective. Energy Economics, 34(5), 1380-1391.

Lima, C. R. A., de Melo, G. R., Stosic, B., & Stosic, T. (2019). Cross-correlations between Brazilian biofuel and food market: Ethanol versus sugar. Physica A: Statistical Mechanics and its Applications, 513, 687-693.

Nazlioglu, S., & Soytas, U. (2012). Oil price, agricultural commodity prices, and the dollar: A panel cointegration and causality analysis. Energy Economics, 34(4), 1098-1104.

Orange, N., & Abaid, N. (2015). A transfer entropy analysis of leader-follower interactions in flying bats. The European Physical Journal Special Topics, 224(17), 3279-3293.

Pessoa, R. V. S., Barreto, I. D. de C., Araújo, L. da S., Moreira, G. R., Stosic, T., & Stosic, B. (2021). Correlações em séries temporais de preços de frango, soja e milho. Research, Society and Development, 10(4), e20610414019.

Schreiber, T. (2000). Measuring information transfer. Physical review letters, 85(2), 461.

Sensoy, A., Sobaci, C., Sensoy, S., & Alali, F. (2014). Effective transfer entropy approach to information flow between exchange rates and stock markets. Chaos, solitons & fractals, 68, 180-185.

Shaffer, I., & Abaid, N. (2020). Transfer Entropy Analysis of Interactions between Bats Using Position and Echolocation Data. Entropy, 22(10), 1176.

Ursino, M., Ricci, G., & Magosso, E. (2020). Transfer Entropy as a Measure of Brain Connectivity: A Critical Analysis With the Help of Neural Mass Models. Frontiers in computational neuroscience, 14, 45.

Van Milligen, B. P., Birkenmeier, G., Ramisch, M., Estrada, T., Hidalgo, C., & Alonso, A. (2014). Causality detection and turbulence in fusion plasmas. Nuclear Fusion, 54(2), 023011.

Vicente, R., Wibral, M., Lindner, M., & Pipa, G. (2011). Transfer entropy—a model-free measure of effective connectivity for the neurosciences. Journal of computational neuroscience, 30(1), 45-67.

Wing, S., Johnson, J. R., Camporeale, E., & Reeves, G. D. (2016). Information theoretical approach to discovering solar wind drivers of the outer radiation belt. Journal of Geophysical Research: Space Physics, 121(10), 9378-9399.

Published

12/06/2021

How to Cite

PASSO, T. M. P. do; SILVA, J. M. da .; ARAÚJO, . L. da S. .; STOSIC, T. . Information flow in brazilian food market: The influence of the 2008 financial crisis. Research, Society and Development, [S. l.], v. 10, n. 7, p. e2110715978, 2021. DOI: 10.33448/rsd-v10i7.15978. Disponível em: https://rsdjournal.org/index.php/rsd/article/view/15978. Acesso em: 5 nov. 2024.

Issue

Section

Agrarian and Biological Sciences