IDEAS home Printed from https://ideas.repec.org/p/ags/aaea16/236167.html
   My bibliography  Save this paper

An Examination Of The Relationship Between Biodiesel And Soybean Oil Prices Using An Asset Pricing Model

Author

Listed:
  • Carriquiry, Miguel

Abstract

This work utilized a discrete time return model of finance to analyze whether prices changes of soybean oil, the main feedstock for biodiesel production in the US affect the prices of biodiesel. Empirical models of asset pricing attempt to extract information about latent state variables and structural parameters from observed prices. These models, which often involve high dimension latent state variables, can be conveniently estimated using Bayesian methods. Results from this study indicate the price of soybean oil does not have a strong direct impact on the price of biodiesel in the short run, or in a daily basis.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Carriquiry, Miguel, "undated". "An Examination Of The Relationship Between Biodiesel And Soybean Oil Prices Using An Asset Pricing Model," 2016 Annual Meeting, July 31-August 2, Boston, Massachusetts 236167, Agricultural and Applied Economics Association.
  • Handle: RePEc:ags:aaea16:236167
    DOI: 10.22004/ag.econ.236167
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/236167/files/Poster%20and%20cover%20page_Carriquiry_AAEA_2016_Biodiesel%20Soyoil_Biodiesel.pdf
    Download Restriction: no

    File URL: https://libkey.io/10.22004/ag.econ.236167?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
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Miguel Carriquiry, 2007. "Comparative Analysis of the Development of the United States and European Union Biodiesel Industries, A," Center for Agricultural and Rural Development (CARD) Publications 07-bp51, Center for Agricultural and Rural Development (CARD) at Iowa State University.
    2. Jacquier, Eric & Polson, Nicholas G & Rossi, Peter E, 2002. "Bayesian Analysis of Stochastic Volatility Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 69-87, January.
    3. Mallory, Mindy L. & Irwin, Scott H. & Hayes, Dermot J., 2012. "How market efficiency and the theory of storage link corn and ethanol markets," Energy Economics, Elsevier, vol. 34(6), pages 2157-2166.
    4. Jacquier, Eric & Polson, Nicholas G & Rossi, Peter E, 1994. "Bayesian Analysis of Stochastic Volatility Models: Comments: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 12(4), pages 413-417, October.
    5. Sanders, Daniel J. & Balagtas, Joseph V. & Gruere, Guillaume P., 2012. "Revisiting the palm oil boom in Southeast Asia: The role of fuel versus food demand drivers," IFPRI discussion papers 1167, International Food Policy Research Institute (IFPRI).
    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. DAVID E. ALLEN & MICHAEL McALEER & ROBERT J. POWELL & ABHAY K. SINGH, 2018. "Non-Parametric Multiple Change Point Analysis Of The Global Financial Crisis," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., vol. 13(02), pages 1-23, June.
    2. Mumtaz, Haroon & Theodoridis, Konstantinos, 2017. "Common and country specific economic uncertainty," Journal of International Economics, Elsevier, vol. 105(C), pages 205-216.
    3. Jensen, Mark J. & Maheu, John M., 2010. "Bayesian semiparametric stochastic volatility modeling," Journal of Econometrics, Elsevier, vol. 157(2), pages 306-316, August.
    4. Pizer, William A., 1999. "The optimal choice of climate change policy in the presence of uncertainty," Resource and Energy Economics, Elsevier, vol. 21(3-4), pages 255-287, August.
    5. Gallant, A. Ronald & Hsieh, David & Tauchen, George, 1997. "Estimation of stochastic volatility models with diagnostics," Journal of Econometrics, Elsevier, vol. 81(1), pages 159-192, November.
    6. Anthony N. Rezitis & Gregor Kastner, 2021. "On the joint volatility dynamics in dairy markets," Papers 2104.12707, arXiv.org.
    7. Lombardi, Marco J. & Calzolari, Giorgio, 2009. "Indirect estimation of [alpha]-stable stochastic volatility models," Computational Statistics & Data Analysis, Elsevier, vol. 53(6), pages 2298-2308, April.
    8. Alessandri, Piergiorgio & Mumtaz, Haroon, 2019. "Financial regimes and uncertainty shocks," Journal of Monetary Economics, Elsevier, vol. 101(C), pages 31-46.
    9. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2015. "Realtime nowcasting with a Bayesian mixed frequency model with stochastic volatility," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 178(4), pages 837-862, October.
    10. Michal Franta & Jan Libich & Petr Stehlík, 2018. "Tracking Monetary-Fiscal Interactions across Time and Space," International Journal of Central Banking, International Journal of Central Banking, vol. 14(3), pages 167-227, June.
    11. Xi, Yanhui & Peng, Hui & Qin, Yemei & Xie, Wenbiao & Chen, Xiaohong, 2015. "Bayesian analysis of heavy-tailed market microstructure model and its application in stock markets," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 117(C), pages 141-153.
    12. Sentana, Enrique & Calzolari, Giorgio & Fiorentini, Gabriele, 2008. "Indirect estimation of large conditionally heteroskedastic factor models, with an application to the Dow 30 stocks," Journal of Econometrics, Elsevier, vol. 146(1), pages 10-25, September.
    13. Valcarcel, Victor J., 2013. "Exchange rate volatility and the time-varying effects of aggregate shocks," Journal of International Money and Finance, Elsevier, vol. 32(C), pages 822-843.
    14. Michael B. Gordy & SØren Willemann, 2012. "Constant Proportion Debt Obligations: A Postmortem Analysis of Rating Models," Management Science, INFORMS, vol. 58(3), pages 476-492, March.
    15. Gorynin, Ivan & Derrode, Stéphane & Monfrini, Emmanuel & Pieczynski, Wojciech, 2017. "Fast smoothing in switching approximations of non-linear and non-Gaussian models," Computational Statistics & Data Analysis, Elsevier, vol. 114(C), pages 38-46.
    16. Uhlig, H.F.H.V.S., 1996. "Bayesian Vector Autoregressions with Stochastic Volatility," Other publications TiSEM 4fd55395-6830-46a2-9d18-e, Tilburg University, School of Economics and Management.
    17. Motta, Anderson C. O. & Hotta, Luiz K., 2003. "Exact Maximum Likelihood and Bayesian Estimation of the Stochastic Volatility Model," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 23(2), November.
    18. repec:ehu:biltok:5744 is not listed on IDEAS
    19. Topaloglou, Nikolas & Tsionas, Mike G., 2020. "Stochastic dominance tests," Journal of Economic Dynamics and Control, Elsevier, vol. 112(C).
    20. Asai, Manabu & McAleer, Michael, 2015. "Forecasting co-volatilities via factor models with asymmetry and long memory in realized covariance," Journal of Econometrics, Elsevier, vol. 189(2), pages 251-262.
    21. Yu, Jun, 2012. "A semiparametric stochastic volatility model," Journal of Econometrics, Elsevier, vol. 167(2), pages 473-482.

    More about this item

    Keywords

    Demand and Price Analysis; Research Methods/ Statistical Methods;

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices

    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:aaea16:236167. 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/aaeaaea.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.