IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v513y2019icp687-693.html
   My bibliography  Save this article

Cross-correlations between Brazilian biofuel and food market: Ethanol versus sugar

Author

Listed:
  • Lima, Cristiane Rocha Albuquerque
  • de Melo, Gabriel Rivas
  • Stosic, Borko
  • Stosic, Tatijana

Abstract

We investigate intrinsic correlations between Brazilian energy (ethanol) and food market (sugar) by applying recently introduced method Detrended partial cross correlation analysis (DPCCA) on returns and volatility series of sugar, ethanol and crude oil prices. The results show that intrinsic cross-correlations between ethanol and sugar are positive and increase with time scale for both returns and volatility, indicating that the diversion of sugar cane for biofuel production (due to the variations in ethanol prices), affects more sugar prices than the price movements in external energy market. Ethanol/oil volatility series and sugar/oil return series do not show correlations for small scales, while at larger scales intrinsic correlations become negative. The intrinsic correlations in oil/ethanol return series and oil/sugar volatility series show similar behavior as corresponding ethanol/sugar series, indicating the existence of strong correlations between sugar and ethanol price variations.

Suggested Citation

  • Lima, Cristiane Rocha Albuquerque & de Melo, Gabriel Rivas & Stosic, Borko & Stosic, Tatijana, 2019. "Cross-correlations between Brazilian biofuel and food market: Ethanol versus sugar," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 513(C), pages 687-693.
  • Handle: RePEc:eee:phsmap:v:513:y:2019:i:c:p:687-693
    DOI: 10.1016/j.physa.2018.08.080
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437118310227
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

    File URL: https://libkey.io/10.1016/j.physa.2018.08.080?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Teresa Serra & David Zilberman & José Gil, 2011. "Price volatility in ethanol markets," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 38(2), pages 259-280, June.
    2. Kristoufek, Ladislav, 2015. "Power-law correlations in finance-related Google searches, and their cross-correlations with volatility and traded volume: Evidence from the Dow Jones Industrial components," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 428(C), pages 194-205.
    3. He, Ling-Yun & Chen, Shu-Peng, 2010. "Are developed and emerging agricultural futures markets multifractal? A comparative perspective," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(18), pages 3828-3836.
    4. Trujillo-Barrera, Andres & Mallory, Mindy L. & Garcia, Philip, 2012. "Volatility Spillovers in U.S. Crude Oil, Ethanol, and Corn Futures Markets," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 37(2), pages 1-16, August.
    5. Kristoufek, Ladislav & Vosvrda, Miloslav, 2014. "Commodity futures and market efficiency," Energy Economics, Elsevier, vol. 42(C), pages 50-57.
    6. Philip Abbott & Adeline Borot de Battisti, 2011. "Recent Global Food Price Shocks: Causes, Consequences and Lessons for African Governments and Donors-super- †," Journal of African Economies, Centre for the Study of African Economies, vol. 20(suppl_1), pages -62, May.
    7. Kristoufek, Ladislav & Janda, Karel & Zilberman, David, 2012. "Correlations between biofuels and related commodities before and during the food crisis: A taxonomy perspective," Energy Economics, Elsevier, vol. 34(5), pages 1380-1391.
    8. Ladislav Kristoufek & Karel Janda & David Zilberman, 2013. "Regime-dependent topological properties of biofuels networks," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 86(2), pages 1-12, February.
    9. Christian A. Gregory & Alisha Coleman-Jensen, 2013. "Do High Food Prices Increase Food Insecurity in the United States?," Applied Economic Perspectives and Policy, Agricultural and Applied Economics Association, vol. 35(4), pages 679-707.
    10. Machado Filho, A. & da Silva, M.F. & Zebende, G.F., 2014. "Autocorrelation and cross-correlation in time series of homicide and attempted homicide," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 400(C), pages 12-19.
    11. Shen, Chen-hua & Li, Cao-ling, 2016. "An analysis of the intrinsic cross-correlations between API and meteorological elements using DPCCA," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 446(C), pages 100-109.
    12. Zunino, Luciano & Tabak, Benjamin M. & Serinaldi, Francesco & Zanin, Massimiliano & Pérez, Darío G. & Rosso, Osvaldo A., 2011. "Commodity predictability analysis with a permutation information theory approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(5), pages 876-890.
    13. Kelvin Balcombe & George Rapsomanikis, 2008. "Bayesian Estimation and Selection of Nonlinear Vector Error Correction Models: The Case of the Sugar-Ethanol-Oil Nexus in Brazil," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 90(3), pages 658-668.
    14. B. M. Tabak & T. R. Serra & D. O. Cajueiro, 2010. "Topological properties of commodities networks," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 74(2), pages 243-249, March.
    15. Chiu, Fan-Ping & Hsu, Chia-Sheng & Ho, Alan & Chen, Chi-Chung, 2016. "Modeling the price relationships between crude oil, energy crops and biofuels," Energy, Elsevier, vol. 109(C), pages 845-857.
    16. Serra, Teresa & Zilberman, David, 2013. "Biofuel-related price transmission literature: A review," Energy Economics, Elsevier, vol. 37(C), pages 141-151.
    17. Lin, Min & Wang, Gang-Jin & Xie, Chi & Stanley, H. Eugene, 2018. "Cross-correlations and influence in world gold markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 504-512.
    18. Julian Lampietti & Sean Michaels & Nick Magnan, 2009. "Improving Food Security in Arab Countries," World Bank Publications - Reports 10992, The World Bank Group.
    19. Siqueira, Erinaldo Leite & Stošić, Tatijana & Bejan, Lucian & Stošić, Borko, 2010. "Correlations and cross-correlations in the Brazilian agrarian commodities and stocks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(14), pages 2739-2743.
    20. Zebende, G.F., 2011. "DCCA cross-correlation coefficient: Quantifying level of cross-correlation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(4), pages 614-618.
    21. Vassoler, R.T. & Zebende, G.F., 2012. "DCCA cross-correlation coefficient apply in time series of air temperature and air relative humidity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(7), pages 2438-2443.
    22. Liu, Li, 2014. "Cross-correlations between crude oil and agricultural commodity markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 395(C), pages 293-302.
    23. Marc F. Bellemare, 2015. "Rising Food Prices, Food Price Volatility, and Social Unrest," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 97(1), pages 1-21.
    24. Xi-Yuan Qian & Ya-Min Liu & Zhi-Qiang Jiang & Boris Podobnik & Wei-Xing Zhou & H. Eugene Stanley, 2015. "Detrended partial cross-correlation analysis of two nonstationary time series influenced by common external forces," Papers 1504.02435, arXiv.org, revised Apr 2015.
    25. Dusan Drabik & Harry De Gorter & David R. Just & Govinda R. Timilsina, 2015. "The Economics of Brazil’s Ethanol-Sugar Markets, Mandates, and Tax Exemptions," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 97(5), pages 1433-1450.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Areola Hernandez, Jose & Uddin, Gazi Salah & Dutta, Anupam & Ahmed, Ali & Kang, Sang Hoon, 2020. "Are ethanol markets globalized or regionalized?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 551(C).
    2. Li, Bao-Gen & Ling, Dian-Yi & Yu, Zu-Guo, 2021. "Multifractal temporally weighted detrended partial cross-correlation analysis of two non-stationary time series affected by common external factors," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 573(C).
    3. Carpio, Lucio Guido Tapia, 2019. "The effects of oil price volatility on ethanol, gasoline, and sugar price forecasts," Energy, Elsevier, vol. 181(C), pages 1012-1022.
    4. Kocak, Emrah & Bilgili, Faik & Bulut, Umit & Kuskaya, Sevda, 2022. "Is ethanol production responsible for the increase in corn prices?," Renewable Energy, Elsevier, vol. 199(C), pages 689-696.
    5. de Araujo, Fernando Henrique Antunes & Bejan, Lucian & Stosic, Borko & Stosic, Tatijana, 2020. "An analysis of Brazilian agricultural commodities using permutation – information theory quantifiers: The influence of food crisis," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    6. Diniz-Maganini, Natalia & Diniz, Eduardo H. & Rasheed, Abdul A., 2021. "Bitcoin’s price efficiency and safe haven properties during the COVID-19 pandemic: A comparison," Research in International Business and Finance, Elsevier, vol. 58(C).
    7. Cheng, Natalie Fang Ling & Hasanov, Akram Shavkatovich & Poon, Wai Ching & Bouri, Elie, 2023. "The US-China trade war and the volatility linkages between energy and agricultural commodities," Energy Economics, Elsevier, vol. 120(C).
    8. Cao, Guangxi & Xie, Wenhao, 2022. "Detrended multiple moving average cross-correlation analysis and its application in the correlation measurement of stock market in Shanghai, Shenzhen, and Hong Kong," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 590(C).
    9. Bilgili, Faik & Koçak, Emrah & Kuşkaya, Sevda & Bulut, Ümit, 2020. "Estimation of the co-movements between biofuel production and food prices: A wavelet-based analysis," Energy, Elsevier, vol. 213(C).
    10. Bilgili, Faik & Kocak, Emrah & Kuskaya, Sevda & Bulut, Umit, 2022. "Co-movements and causalities between ethanol production and corn prices in the USA: New evidence from wavelet transform analysis," Energy, Elsevier, vol. 259(C).
    11. Saeed Solaymani, 2022. "Global Energy Price Volatility and Agricultural Commodity Prices in Malaysia," Biophysical Economics and Resource Quality, Springer, vol. 7(4), pages 1-21, December.
    12. Yahya, Muhammad & Dutta, Anupam & Bouri, Elie & Wadström, Christoffer & Uddin, Gazi Salah, 2022. "Dependence structure between the international crude oil market and the European markets of biodiesel and rapeseed oil," Renewable Energy, Elsevier, vol. 197(C), pages 594-605.
    13. Derick David Quintino & Heloisa Lee Burnquist & Paulo Jorge Silveira Ferreira, 2021. "Carbon Emissions and Brazilian Ethanol Prices: Are They Correlated? An Econophysics Study," Sustainability, MDPI, vol. 13(22), pages 1-18, November.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. de Araujo, Fernando Henrique Antunes & Bejan, Lucian & Stosic, Borko & Stosic, Tatijana, 2020. "An analysis of Brazilian agricultural commodities using permutation – information theory quantifiers: The influence of food crisis," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    2. Sukpitak, Jessada & Hengpunya, Varagorn, 2016. "The influence of trading volume on market efficiency: The DCCA approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 458(C), pages 259-265.
    3. Karel Janda & Ladislav Kristoufek, 2019. "The relationship between fuel and food prices: Methods, outcomes, and lessons for commodity price risk management," CAMA Working Papers 2019-20, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    4. Ladislav Kristoufek & Karel Janda & David Zilberman, 2015. "Co-movements of Ethanol Related Prices: Evidence from Brazil and the USA," CAMA Working Papers 2015-11, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    5. Albarracín E., Eva Susana & Gamboa, Juan C. Rodríguez & Marques, Elaine C.M. & Stosic, Tatijana, 2019. "Complexity analysis of Brazilian agriculture and energy market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 933-941.
    6. Nascimento Filho, A.S. & Pereira, E.J.A.L. & Ferreira, Paulo & Murari, T.B. & Moret, M.A., 2018. "Cross-correlation analysis on Brazilian gasoline retail market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 508(C), pages 550-557.
    7. Ladislav Kristoufek & Karel Janda & David Zilberman, 2013. "Regime-dependent topological properties of biofuels networks," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 86(2), pages 1-12, February.
    8. Ondřej Filip & Karel Janda & Ladislav Krištoufek, 2018. "Ceny biopaliv a souvisejících komodit: analýza s použitím metod minimální kostry grafu a hierarchických stromů [Prices of Biofuels and Related Commodities: an Analysis Using Methods of Minimum Span," Politická ekonomie, Prague University of Economics and Business, vol. 2018(2), pages 218-239.
    9. Dutta, Srimonti & Ghosh, Dipak & Samanta, Shukla, 2014. "Multifractal detrended cross-correlation analysis of gold price and SENSEX," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 413(C), pages 195-204.
    10. Guedes, E. & Dionísio, A. & Ferreira, P.J. & Zebende, G.F., 2017. "DCCA cross-correlation in blue-chips companies: A view of the 2008 financial crisis in the Eurozone," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 479(C), pages 38-47.
    11. Filip, Ondrej & Janda, Karel & Kristoufek, Ladislav & Zilberman, David, 2019. "Food versus fuel: An updated and expanded evidence," Energy Economics, Elsevier, vol. 82(C), pages 152-166.
    12. Giray GOZGOR & Cahit MEMIS, 2015. "Price volatility spillovers among agricultural commodity and crude oil markets: Evidence from the range-based estimator," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 61(5), pages 214-221.
    13. Shen, Chenhua, 2017. "A comparison of principal components using TPCA and nonstationary principal component analysis on daily air-pollutant concentration series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 467(C), pages 453-464.
    14. Saghaian, Sayed H. & Nemati, Mehdi & Walters, Cory G. & Chen, Bo, 2017. "Asymmetric Price Volatility Interaction between U.S. Food and Energy Markets," 2017 Annual Meeting, July 30-August 1, Chicago, Illinois 258240, Agricultural and Applied Economics Association.
    15. Karel Janda & Ladislav Krištoufek, 2019. "The Relationship Between Fuel and Food Prices: Methods and Outcomes," Annual Review of Resource Economics, Annual Reviews, vol. 11(1), pages 195-216, October.
    16. Shen, Chenhua, 2019. "The influence of a scaling exponent on ρDCCA: A spatial cross-correlation pattern of precipitation records over eastern China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 516(C), pages 579-590.
    17. Kumar, Pawan & Singh, Vipul Kumar & Rao, Sandeep, 2023. "Does the substitution effect lead to feedback effect linkage between ethanol, crude oil, and soft agricultural commodities?," Energy Economics, Elsevier, vol. 119(C).
    18. Vacha, Lukas & Janda, Karel & Kristoufek, Ladislav & Zilberman, David, 2013. "Time–frequency dynamics of biofuel–fuel–food system," Energy Economics, Elsevier, vol. 40(C), pages 233-241.
    19. Paiva, Aureliano Sancho Souza & Rivera-Castro, Miguel Angel & Andrade, Roberto Fernandes Silva, 2018. "DCCA analysis of renewable and conventional energy prices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 1408-1414.
    20. Ferreira, Paulo & Loures, Luís & Nunes, José & Brito, Paulo, 2018. "Are renewable energy stocks a possibility to diversify portfolios considering an environmentally friendly approach? The view of DCCA correlation coefficient," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 675-681.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:513:y:2019:i:c:p:687-693. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.