IDEAS home Printed from https://ideas.repec.org/p/ags/saea17/252741.html
   My bibliography  Save this paper

Time Varying Correlation Research Among Corn, Ethanol, And Gasoline: Copula –Garch Approach

Author

Listed:
  • Ha, Sang su
  • Welch, J. Mark
  • Anderson, David P.

Abstract

No abstract is available for this item.

Suggested Citation

  • Ha, Sang su & Welch, J. Mark & Anderson, David P., 2016. "Time Varying Correlation Research Among Corn, Ethanol, And Gasoline: Copula –Garch Approach," 2017 Annual Meeting, February 4-7, 2017, Mobile, Alabama 252741, Southern Agricultural Economics Association.
    • Handle: RePEc:ags:saea17:252741
    DOI: 10.22004/ag.econ.252741
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/252741/files/SAEA2017_Sang%20su%20Ha.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.22004/ag.econ.252741?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
    ---><---

    References listed on IDEAS

    as
    1. Aas, Kjersti & Czado, Claudia & Frigessi, Arnoldo & Bakken, Henrik, 2009. "Pair-copula constructions of multiple dependence," Insurance: Mathematics and Economics, Elsevier, vol. 44(2), pages 182-198, April.
    2. Serra, Teresa, 2011. "Volatility spillovers between food and energy markets: A semiparametric approach," Energy Economics, Elsevier, vol. 33(6), pages 1155-1164.
    3. Joe, Harry, 2005. "Asymptotic efficiency of the two-stage estimation method for copula-based models," Journal of Multivariate Analysis, Elsevier, vol. 94(2), pages 401-419, June.
    4. Seth Meyer & Wyatt Thompson, 2012. "How Do Biofuel Use Mandates Cause Uncertainty? United States Environmental Protection Agency Cellulosic Waiver Options," Applied Economic Perspectives and Policy, Agricultural and Applied Economics Association, vol. 34(4), pages 570-586.
    5. Jondeau, Eric & Rockinger, Michael, 2006. "The Copula-GARCH model of conditional dependencies: An international stock market application," Journal of International Money and Finance, Elsevier, vol. 25(5), pages 827-853, August.
    6. Condon, Nicole & Klemick, Heather & Wolverton, Ann, 2015. "Impacts of ethanol policy on corn prices: A review and meta-analysis of recent evidence," Food Policy, Elsevier, vol. 51(C), pages 63-73.
    7. Koirala, Krishna H. & Mishra, Ashok K. & D'Antoni, Jeremy M. & Mehlhorn, Joey E., 2015. "Energy prices and agricultural commodity prices: Testing correlation using copulas method," Energy, Elsevier, vol. 81(C), pages 430-436.
    8. Seth Meyer & Wyatt Thompson, 2012. "How Do Biofuel Use Mandates Cause Uncertainty? United States Environmental Protection Agency Cellulosic Waiver Options," Applied Economic Perspectives and Policy, Agricultural and Applied Economics Association, vol. 34(4), pages 570-586.
    9. Christopher R. Knittel and Aaron Smith, 2015. "Ethanol Production and Gasoline Prices: A Spurious Correlation," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1).
    10. Andrew J. Patton, 2006. "Modelling Asymmetric Exchange Rate Dependence," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 47(2), pages 527-556, May.
    Full references (including those not matched with items on IDEAS)

    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. Patton, Andrew, 2013. "Copula Methods for Forecasting Multivariate Time Series," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 899-960, Elsevier.
    2. BenSaïda, Ahmed, 2018. "The contagion effect in European sovereign debt markets: A regime-switching vine copula approach," International Review of Financial Analysis, Elsevier, vol. 58(C), pages 153-165.
    3. Zhang, Ran & Czado, Claudia & Min, Aleksey, 2011. "Efficient maximum likelihood estimation of copula based meta t-distributions," Computational Statistics & Data Analysis, Elsevier, vol. 55(3), pages 1196-1214, March.
    4. Li, Feng & Kang, Yanfei, 2018. "Improving forecasting performance using covariate-dependent copula models," International Journal of Forecasting, Elsevier, vol. 34(3), pages 456-476.
    5. Warshaw, Evan, 2019. "Extreme dependence and risk spillovers across north american equity markets," The North American Journal of Economics and Finance, Elsevier, vol. 47(C), pages 237-251.
    6. Maziar Sahamkhadam, 2021. "Dynamic copula-based expectile portfolios," Journal of Asset Management, Palgrave Macmillan, vol. 22(3), pages 209-223, May.
    7. Pavel Krupskii, 2017. "Copula-based measures of reflection and permutation asymmetry and statistical tests," Statistical Papers, Springer, vol. 58(4), pages 1165-1187, December.
    8. Derumigny Alexis & Fermanian Jean-David, 2017. "About tests of the “simplifying” assumption for conditional copulas," Dependence Modeling, De Gruyter, vol. 5(1), pages 154-197, August.
    9. Chen, Wei-Peng & Choudhry, Taufiq & Wu, Chih-Chiang, 2013. "The extreme value in crude oil and US dollar markets," Journal of International Money and Finance, Elsevier, vol. 36(C), pages 191-210.
    10. Koliai, Lyes, 2016. "Extreme risk modeling: An EVT–pair-copulas approach for financial stress tests," Journal of Banking & Finance, Elsevier, vol. 70(C), pages 1-22.
    11. Thomas B. Fomby & Jeffery W. Gunther & Jian Hu, 2012. "Return Dependence and the Limits of Product Diversification in Financial Firms," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 44(6), pages 1151-1183, September.
    12. Grundke, Peter & Polle, Simone, 2012. "Crisis and risk dependencies," European Journal of Operational Research, Elsevier, vol. 223(2), pages 518-528.
    13. Stanislav Anatolyev & Renat Khabibullin & Artem Prokhorov, 2012. "Reconstructing high dimensional dynamic distributions from distributions of lower dimension," Working Papers 12003, Concordia University, Department of Economics.
    14. Hofert, Marius & Prasad, Avinash & Zhu, Mu, 2022. "Multivariate time-series modeling with generative neural networks," Econometrics and Statistics, Elsevier, vol. 23(C), pages 147-164.
    15. Tong, Bin & Diao, Xundi & Wu, Chongfeng, 2015. "Modeling asymmetric and dynamic dependence of overnight and daytime returns: An empirical evidence from China Banking Sector," Economic Modelling, Elsevier, vol. 51(C), pages 366-382.
    16. De Lira Salvatierra, Irving & Patton, Andrew J., 2015. "Dynamic copula models and high frequency data," Journal of Empirical Finance, Elsevier, vol. 30(C), pages 120-135.
    17. Hernández-Lobato, José Miguel & Suárez, Alberto, 2011. "Semiparametric bivariate Archimedean copulas," Computational Statistics & Data Analysis, Elsevier, vol. 55(6), pages 2038-2058, June.
    18. Gregor Weiß, 2013. "Copula-GARCH versus dynamic conditional correlation: an empirical study on VaR and ES forecasting accuracy," Review of Quantitative Finance and Accounting, Springer, vol. 41(2), pages 179-202, August.
    19. Condon, Nicole & Klemick, Heather & Wolverton, Ann, 2015. "Impacts of ethanol policy on corn prices: A review and meta-analysis of recent evidence," Food Policy, Elsevier, vol. 51(C), pages 63-73.
    20. Siburg, Karl Friedrich & Stoimenov, Pavel & Weiß, Gregor N.F., 2015. "Forecasting portfolio-Value-at-Risk with nonparametric lower tail dependence estimates," Journal of Banking & Finance, Elsevier, vol. 54(C), pages 129-140.

    More about this item

    Keywords

    Resource /Energy Economics and Policy;

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:ags:saea17:252741. 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: AgEcon Search (email available below). General contact details of provider: https://edirc.repec.org/data/saeaaea.html .

    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.