Time-varying hedge ratios in linked agricultural markets
AbstractPurpose – The purpose of this paper is to examine the potential gains in hedge ratio calculation for agricultural commodities by incorporating market linkages and prices of related commodities into the hedge ratio estimation process. Design/methodology/approach – A vector autoregressive multivariate generalized autoregressive conditional heteroskedasticity (VAR-MGARCH) model is used to construct a time-varying correlation matrix for commodity prices across linked markets and across linked commodities. The MGARCH model is estimated using a two-step approach, which allows for a large system of related prices to be estimated. Findings – In-sample and out-of-sample portfolio variance comparison among no hedge, bivariate GARCH, and MGARCH models indicates that hedge ratios estimated using the MGARCH approach reduce agricultural producers' and commercial consumers' risks in futures market participation. Research limitations/implications – The application is limited to an examination of Montana wheat markets. Practical implications – Agricultural producers who use futures markets to reduce market risk will have a better method for determining hedging positions, because MGARCH estimated hedge ratios incorporate more information than hedge ratios estimated using existing practices. Social implications – Portfolio variance reduction is analogous to utility improvement for agricultural producers. More efficient hedging strategies can lead to better implementation of futures markets and increased social welfare. Originality/value – This research substantially extends current literature on agricultural hedge strategies by illustrating the advantages of using an hedge ratio estimation approach that incorporates important information about prices at linked markets and prices of other commodities. Providing evidence that market portfolio variance can be lowered using the multivariate estimation approach, the research offers commercial agricultural producers and consumers a practical tool for improving futures market strategies.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoArticle provided by Emerald Group Publishing in its journal Agricultural Finance Review.
Volume (Year): 71 (2011)
Issue (Month): 2 (July)
Contact details of provider:
Web page: http://www.emeraldinsight.com
Postal: Emerald Group Publishing, Howard House, Wagon Lane, Bingley, BD16 1WA, UK
Find related papers by JEL classification:
- Q18 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Agricultural Policy; Food Policy
- D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
- Q12 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Micro Analysis of Farm Firms, Farm Households, and Farm Input Markets
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Engle, Robert F & Sheppard, Kevin K, 2001.
"Theoretical and Empirical Properties of Dynamic Conditional Correlation Multivariate GARCH,"
University of California at San Diego, Economics Working Paper Series
qt5s2218dp, Department of Economics, UC San Diego.
- Robert F. Engle & Kevin Sheppard, 2001. "Theoretical and Empirical properties of Dynamic Conditional Correlation Multivariate GARCH," NBER Working Papers 8554, National Bureau of Economic Research, Inc.
- Lence, Sergio H., 1995.
"On the optimal hedge under unbiased futures prices,"
Elsevier, vol. 47(3-4), pages 385-388, March.
- Lence, Sergio H., 1995. "On the Optimal Hedge Under Unbiased Futures Prices," Staff General Research Papers 5115, Iowa State University, Department of Economics.
- GianCarlo Moschini & Robert J. Myers, 2001.
"Testing for Constant Hedge Ratios in Commodity Markets: A Multivariate GARCH Approach,"
Center for Agricultural and Rural Development (CARD) Publications
01-wp268, Center for Agricultural and Rural Development (CARD) at Iowa State University.
- Moschini, GianCarlo & Myers, Robert J., 2002. "Testing for constant hedge ratios in commodity markets: a multivariate GARCH approach," Journal of Empirical Finance, Elsevier, vol. 9(5), pages 589-603, December.
- Moschini, GianCarlo & Myers, Robert J., 2002. "Testing for Constant Hedge Ratios in Commodity Markets: A Multivariate Garch Approach," Staff General Research Papers 1945, Iowa State University, Department of Economics.
- Choudhry, Taufiq, 2009. "Short-run deviations and time-varying hedge ratios: Evidence from agricultural futures markets," International Review of Financial Analysis, Elsevier, vol. 18(1-2), pages 58-65, March.
- Peter S. Sephton, 1993. "Optimal Hedge Ratios at the Winnipeg Commodity Exchange," Canadian Journal of Economics, Canadian Economics Association, vol. 26(1), pages 175-93, February.
- Anil K. Bera & Philip Garcia & Jae-Sun Roh, 1997. "Estimation of Time-Varying Hedge Ratios for Corn and Soybeans: BGARCH and Random Coefficient Approaches," Finance 9712007, EconWPA.
- BAUWENS, Luc & LAURENT, Sébastien & ROMBOUTS, Jeroen VK, .
"Multivariate GARCH models: a survey,"
CORE Discussion Papers RP
-1847, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
- Baillie, R.T. & Myers, R.J., 1989.
"Modeling Commodity Price Distribution And Estimating The Optimal Futures Hedge,"
8814, Michigan State - Econometrics and Economic Theory.
- Baillie, R.T. & Myers, R.J., 1989. "Modeling Commodity Price Distributions And Estimating The Optimal Futures Hedge," Papers 201, Columbia - Center for Futures Markets.
- Bossaerts, Peter & Hillion, Pierre, 1999. "Implementing Statistical Criteria to Select Return Forecasting Models: What Do We Learn?," Review of Financial Studies, Society for Financial Studies, vol. 12(2), pages 405-28.
- Engle, Robert F. & Kroner, Kenneth F., 1995. "Multivariate Simultaneous Generalized ARCH," Econometric Theory, Cambridge University Press, vol. 11(01), pages 122-150, February.
- Bollerslev, Tim & Engle, Robert F & Wooldridge, Jeffrey M, 1988. "A Capital Asset Pricing Model with Time-Varying Covariances," Journal of Political Economy, University of Chicago Press, vol. 96(1), pages 116-31, February.
- Berg, Nathan & Gu, Anthony Y. & Lien, Donald, 2007. "Dynamic correlation: A tool hedging house-price risk?," MPRA Paper 26368, University Library of Munich, Germany.
- Baillie, Richard T & Myers, Robert J, 1991. "Bivariate GARCH Estimation of the Optimal Commodity Futures Hedge," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 6(2), pages 109-24, April-Jun.
- Kroner, Kenneth F. & Sultan, Jahangir, 1993. "Time-Varying Distributions and Dynamic Hedging with Foreign Currency Futures," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 28(04), pages 535-551, December.
- Ling, Shiqing & McAleer, Michael, 2003.
"Asymptotic Theory For A Vector Arma-Garch Model,"
Cambridge University Press, vol. 19(02), pages 280-310, April.
- Lien, Donald & Tse, Y K, 2002. " Some Recent Developments in Futures Hedging," Journal of Economic Surveys, Wiley Blackwell, vol. 16(3), pages 357-96, July.
- Michael Cooper & Roberto C. Gutierrez, Jr. & Bill Marcum, 2005. "On the Predictability of Stock Returns in Real Time," The Journal of Business, University of Chicago Press, vol. 78(2), pages 469-500, March.
- Michael S. Haigh & Matthew T. Holt, 2000. "Hedging Multiple Price Uncertainty in International Grain Trade," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 82(4), pages 881-896.
- Zanotti, Giovanna & Gabbi, Giampaolo & Geranio, Manuela, 2010. "Hedging with futures: Efficacy of GARCH correlation models to European electricity markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 20(2), pages 135-148, April.
- John F. Geweke, 1994. "Bayesian comparison of econometric models," Working Papers 532, Federal Reserve Bank of Minneapolis.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Louise Lister).
If references are entirely missing, you can add them using this form.