IDEAS home Printed from
   My bibliography  Save this paper

What Explains High Commodity Price Volatility? Estimating a Unified Model of Common and Commodity-Specific, High- and Low-Frequency Factors


  • Karali, Berna
  • Power, Gabriel J.


We estimate a model of common and commodity-specific, high- and low-frequency factors, built on the spline-GARCH model of Engle and Rangel (2008) to explain the period of exceptionally high price volatility in commodity markets during 2006-2008. We find that decomposing realized volatility into high- and low-frequency components reveals the impact of slowly-evolving macroeconomic variables on the price volatility. Further, we find that while macroeconomic variables have similar effects within the same commodity category (e.g., storable agricultural), they have different effects across commodity groups (e.g., live stock versus energy).

Suggested Citation

  • Karali, Berna & Power, Gabriel J., 2009. "What Explains High Commodity Price Volatility? Estimating a Unified Model of Common and Commodity-Specific, High- and Low-Frequency Factors," 2009 Annual Meeting, July 26-28, 2009, Milwaukee, Wisconsin 49576, Agricultural and Applied Economics Association.
  • Handle: RePEc:ags:aaea09:49576

    Download full text from publisher

    File URL:
    Download Restriction: no

    References listed on IDEAS

    1. Robert S. Pindyck, 1994. "Inventories and the Short-Run Dynamics of Commodity Prices," RAND Journal of Economics, The RAND Corporation, vol. 25(1), pages 141-159, Spring.
    2. Robert F. Engle & Jose Gonzalo Rangel, 2008. "The Spline-GARCH Model for Low-Frequency Volatility and Its Global Macroeconomic Causes," Review of Financial Studies, Society for Financial Studies, vol. 21(3), pages 1187-1222, May.
    3. Ser-Huang Poon & Clive W.J. Granger, 2003. "Forecasting Volatility in Financial Markets: A Review," Journal of Economic Literature, American Economic Association, vol. 41(2), pages 478-539, June.
    4. Berna Karali & Walter N. Thurman, 2009. "Announcement effects and the theory of storage: an empirical study of lumber futures," Agricultural Economics, International Association of Agricultural Economists, vol. 40(4), pages 421-436, July.
    5. Ian Tonks & Jane Black, 1999. "Time Series Volatility Commodity Futures Prices," FMG Discussion Papers dp331, Financial Markets Group.
    6. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    7. Mark, Darrell R. & Brorsen, B. Wade & Anderson, Kim B. & Small, Rebecca M., 2008. "Price Risk Management Alternatives for Farmers in the Absence of Forward Contracts with Grain Merchants," Choices, Agricultural and Applied Economics Association, vol. 23(2).
    Full references (including those not matched with items on IDEAS)


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

    Cited by:

    1. Ott, Herve, 2012. "Which factors drive which volatility in the grain sector?," 123rd Seminar, February 23-24, 2012, Dublin, Ireland 122486, European Association of Agricultural Economists.
    2. Hervé Ott, 2014. "Extent and possible causes of intrayear agricultural commodity price volatility," Agricultural Economics, International Association of Agricultural Economists, vol. 45(2), pages 225-252, March.
    3. Semei Coronado-Ram'irez & Pedro Celso-Arellano & Omar Rojas, 2014. "Adaptive Market Efficiency of Agricultural Commodity Futures Contracts," Papers 1412.8017,, revised Mar 2015.
    4. von Braun, Joachim & Tadesse, Getaw, 2012. "Global Food Price Volatility and Spikes: An Overview of Costs, Causes, and Solutions," Discussion Papers 120021, University of Bonn, Center for Development Research (ZEF).

    More about this item


    volatility; spline-GARCH; futures markets; Agricultural Finance; Demand and Price Analysis;

    NEP fields

    This paper has been announced in the following NEP Reports:


    Access and download statistics


    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:aaea09:49576. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (AgEcon Search). General contact details of provider: .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.