IDEAS home Printed from https://ideas.repec.org/a/eee/eneeco/v57y2016icp59-65.html
   My bibliography  Save this article

Hedging strategy for ethanol processing with copula distributions

Author

Listed:
  • Awudu, Iddrisu
  • Wilson, William
  • Dahl, Bruce

Abstract

It has become important for ethanol producers to hedge input and output price risks. The purpose of this paper is to analyze an ethanol-producing firm's strategy to reduce price risks for inputs and outputs. Corn is the primary input, and the outputs are ethanol, corn oil, distillers' dried grains (DDGs), and renewable identification numbers (RINs). A theoretical model is developed including margins and risk is measured using value at risk (VaR). An empirical model is developed and extended to VaR using copulas to analyze the marginal distribution and dependence structure for input and output prices on margins. Efficient frontier curves analyzing VaR with and without copula are discussed. The results compare varying risk-strategy measures for long corn, short corn, and combining short and long corn. Sensitivity analyses are conducted for functional changes in the margin as a result of ethanol price changes.

Suggested Citation

  • Awudu, Iddrisu & Wilson, William & Dahl, Bruce, 2016. "Hedging strategy for ethanol processing with copula distributions," Energy Economics, Elsevier, vol. 57(C), pages 59-65.
  • Handle: RePEc:eee:eneeco:v:57:y:2016:i:c:p:59-65
    DOI: 10.1016/j.eneco.2016.04.011
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0140988316300901
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.eneco.2016.04.011?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. Dahlgran, Roger A., 2009. "Inventory and Transformation Hedging Effectiveness in Corn Crushing," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 34(1), pages 1-18, April.
    2. Chang, Chia-Lin & Chen, Li-Hsueh & Hammoudeh, Shawkat & McAleer, Michael, 2012. "Asymmetric adjustments in the ethanol and grains markets," Energy Economics, Elsevier, vol. 34(6), pages 1990-2002.
    3. David A. Hennessy & Harvey E. Lapan, 2002. "The Use of Archimedean Copulas to Model Portfolio Allocations," Mathematical Finance, Wiley Blackwell, vol. 12(2), pages 143-154, April.
    4. Brinker, Adam J. & Parcell, Joseph L. & Dhuyvetter, Kevin C., 2007. "Cross-Hedging Distillers Dried Grains: Exploring Corn and Soybean Meal Futures Contracts," 2007 Conference, April 16-17, 2007, Chicago, Illinois 37567, NCCC-134 Conference on Applied Commodity Price Analysis, Forecasting, and Market Risk Management.
    5. William W. Wilson & William E. Nganje & Robert Wagner, 2006. "Hedging Strategies for Grain Processors," Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, Canadian Agricultural Economics Society/Societe canadienne d'agroeconomie, vol. 54(2), pages 311-326, June.
    6. Qi Fu & Chung-Yee Lee & Chung-Piaw Teo, 2010. "Procurement management using option contracts: random spot price and the portfolio effect," IISE Transactions, Taylor & Francis Journals, vol. 42(11), pages 793-811.
    7. Dahlgran, Roger A., 2005. "Transaction Frequency and Hedging in Commodity Processing," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 30(3), pages 1-20, December.
    8. Genest, Christian & Rémillard, Bruno & Beaudoin, David, 2009. "Goodness-of-fit tests for copulas: A review and a power study," Insurance: Mathematics and Economics, Elsevier, vol. 44(2), pages 199-213, April.
    9. Arnd Huchzermeier & Morris A. Cohen, 1996. "Valuing Operational Flexibility Under Exchange Rate Risk," Operations Research, INFORMS, vol. 44(1), pages 100-113, February.
    10. Brechmann, Eike Christian & Schepsmeier, Ulf, 2013. "Modeling Dependence with C- and D-Vine Copulas: The R Package CDVine," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 52(i03).
    11. Songjiao Chen & William W. Wilson & Ryan Larsen & Bruce Dahl, 2015. "Investing in Agriculture as an Asset Class," Agribusiness, John Wiley & Sons, Ltd., vol. 31(3), pages 353-371, June.
    12. Quintino, Derick David & David, Sergio Adriani, 2013. "Quantitative analysis of feasibility of hydrous ethanol futures contracts in Brazil," Energy Economics, Elsevier, vol. 40(C), pages 927-935.
    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. Nguyen, Quynh Nga & Bedoui, Rihab & Majdoub, Najemeddine & Guesmi, Khaled & Chevallier, Julien, 2020. "Hedging and safe-haven characteristics of Gold against currencies: An investigation based on multivariate dynamic copula theory," Resources Policy, Elsevier, vol. 68(C).
    2. Liu, Pan & Vedenov, Dmitry & Power, Gabriel J., 2017. "Is hedging the crack spread no longer all it's cracked up to be?," Energy Economics, Elsevier, vol. 63(C), pages 31-40.
    3. Sukcharoen, Kunlapath & Leatham, David J., 2017. "Hedging downside risk of oil refineries: A vine copula approach," Energy Economics, Elsevier, vol. 66(C), pages 493-507.
    4. Chai, Shanglei & Zhou, P., 2018. "The Minimum-CVaR strategy with semi-parametric estimation in carbon market hedging problems," Energy Economics, Elsevier, vol. 76(C), pages 64-75.

    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. Rašiová, Barbara & Árendáš, Peter, 2023. "Copula approach to market volatility and technology stocks dependence," Finance Research Letters, Elsevier, vol. 52(C).
    2. Emmanoulides, Christos & Fousekis, Panos, 2014. "Vertical Price Transmission in the US Pork Industry: Evidence from Copula Models," Agricultural Economics Review, Greek Association of Agricultural Economists, vol. 15(1), pages 1-12.
    3. Maziar Sahamkhadam & Andreas Stephan, 2019. "Portfolio optimization based on forecasting models using vine copulas: An empirical assessment for the financial crisis," Papers 1912.10328, arXiv.org.
    4. Songjiao Chen & William Wilson & Ryan Larsen & Bruce Dahl, 2016. "Risk Management for Grain Processors and “Copulas”," Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, Canadian Agricultural Economics Society/Societe canadienne d'agroeconomie, vol. 64(2), pages 365-382, June.
    5. Kajal Lahiri & Liu Yang, 2023. "Predicting binary outcomes based on the pair-copula construction," Empirical Economics, Springer, vol. 64(6), pages 3089-3119, June.
    6. Spencer, Simon & Bredin, Don & Conlon, Thomas, 2018. "Energy and agricultural commodities revealed through hedging characteristics: Evidence from developing and mature markets," Journal of Commodity Markets, Elsevier, vol. 9(C), pages 1-20.
    7. Stavrakoudis, Athanassios & Panagiotou, Dimitrios, 2016. "Price dependence and asymmetric responses between coffee varieties," Agricultural Economics Review, Greek Association of Agricultural Economists, vol. 17(2), June.
    8. Zhichao Zhang & Li Ding & Fan Zhang & Zhuang Zhang, 2015. "Optimal Currency Composition for China's Foreign Reserves: A Copula Approach," The World Economy, Wiley Blackwell, vol. 38(12), pages 1947-1965, December.
    9. Eling, Martin & Jung, Kwangmin, 2018. "Copula approaches for modeling cross-sectional dependence of data breach losses," Insurance: Mathematics and Economics, Elsevier, vol. 82(C), pages 167-180.
    10. Catherine Bruneau & Alexis Flageollet & Zhun Peng, 2020. "Economic and financial risk factors, copula dependence and risk sensitivity of large multi-asset class portfolios," Annals of Operations Research, Springer, vol. 284(1), pages 165-197, January.
    11. Mejdoub, Hanène & Ben Arab, Mounira, 2018. "Impact of dependence modeling of non-life insurance risks on capital requirement: D-Vine Copula approach," Research in International Business and Finance, Elsevier, vol. 45(C), pages 208-218.
    12. Stavrakoudis, Athanassios & Panagiotou, Dimitrios, 2016. "Price dependence between coffee qualities: a copula model to evaluate asymmetric responses," MPRA Paper 75994, University Library of Munich, Germany.
    13. F. Marta L. Di Lascio & Andrea Menapace & Maurizio Righetti, 2020. "Joint and conditional dependence modelling of peak district heating demand and outdoor temperature: a copula-based approach," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 29(2), pages 373-395, June.
    14. Mikhail Semenov & Daulet Smagulov, 2017. "Portfolio Risk Assessment using Copula Models," Papers 1707.03516, arXiv.org.
    15. Penikas, H., 2010. "Financial Applications of Copula-Models," Journal of the New Economic Association, New Economic Association, issue 7, pages 24-44.
    16. Grover, Vaibhav, 2015. "Identifying Dependence Structure among Equities in Indian Markets using Copulas," MPRA Paper 66302, University Library of Munich, Germany.
    17. Jiao Wang & Lima Zhao & Arnd Huchzermeier, 2021. "Operations‐Finance Interface in Risk Management: Research Evolution and Opportunities," Production and Operations Management, Production and Operations Management Society, vol. 30(2), pages 355-389, February.
    18. Feng, Yi & Mu, Yinping & Hu, Benyong & Kumar, Arun, 2014. "Commodity options purchasing and credit financing under capital constraint," International Journal of Production Economics, Elsevier, vol. 153(C), pages 230-237.
    19. Jie Huang & Haiming Zhou & Nader Ebrahimi, 2022. "Bayesian Bivariate Cure Rate Models Using Copula Functions," International Journal of Statistics and Probability, Canadian Center of Science and Education, vol. 11(3), pages 1-9, May.
    20. Daniel Puig & Oswaldo Morales-Nápoles & Fatemeh Bakhtiari & Gissela Landa, 2017. "The accountability imperative for quantifiying the uncertainty of emission forecasts : evidence from Mexico," Working Papers hal-03389325, HAL.

    More about this item

    Keywords

    Ethanol; Hedging; Value at risk; Copula; Efficient frontier;
    All these keywords.

    JEL classification:

    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • Q13 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Agricultural Markets and Marketing; Cooperatives; Agribusiness
    • Q42 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Alternative Energy Sources

    Statistics

    Access and download statistics

    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:eneeco:v:57:y:2016:i:c:p:59-65. 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.elsevier.com/locate/eneco .

    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.