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A State-Space Forecasting Approach to Optimal Intertemporal Cross-Hedging

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  • Tomislav Vukina
  • James L. Anderson

Abstract

Cross-commodity hedging between fishmeal and soybean meal is investigated. The approach uses successively updated out-of-sample forecasts to approximate subjective price expectations, and forecast error variance-covariance matrices to measure risk. Forecasts are generated by state-space models of vector-valued time series. In a stationary environment, uncertainty reduces to the difference between the historical autocovariance of the random process and the variance-covariance of out-of-sample forecasts. Results indicate that weakly risk-averse agents can increase average marketing returns within acceptable risk levels by combining information from price forecasting models with an appropriate hedging strategy.

Suggested Citation

  • Tomislav Vukina & James L. Anderson, 1993. "A State-Space Forecasting Approach to Optimal Intertemporal Cross-Hedging," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 75(2), pages 416-424.
  • Handle: RePEc:oup:ajagec:v:75:y:1993:i:2:p:416-424.
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    File URL: http://hdl.handle.net/10.2307/1242926
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    Cited by:

    1. Rahman, Shaikh Mahfuzur & Turner, Steven C. & Costa, Ecio de Farias, 2001. "Cross-Hedging Cottonseed Meal," Journal of Agribusiness, Agricultural Economics Association of Georgia, vol. 0(Number 2), pages 1-9.
    2. Asche, Frank & Misund, Bard & Oglend, Atle, 2015. "The Spot-Forward Relationship in the Atlantic Salmon Market," UiS Working Papers in Economics and Finance 2015/16, University of Stavanger.
    3. Michael S. Haigh & Matthew T. Holt, 2002. "Combining time-varying and dynamic multi-period optimal hedging models," European Review of Agricultural Economics, Foundation for the European Review of Agricultural Economics, vol. 29(4), pages 471-500, December.
    4. Tejeda, Hernan A., 2012. "Time-Varying Price Interactions and Risk Management in Livestock Feed Markets – Determining the Ethanol Surge Effect," 2012 Annual Meeting, August 12-14, 2012, Seattle, Washington 124956, Agricultural and Applied Economics Association.
    5. Vadhindran K. Rao, 2011. "Multiperiod Hedging using Futures: Mean Reversion and the Optimal Hedging Path," Journal of Risk and Financial Management, MDPI, Open Access Journal, vol. 4(1), pages 1-29, December.
    6. Nyambane, Gerald G. & Hanson, Steven D. & Myers, Robert J. & Black, J. Roy, 2002. "Dynamic Risk Management Under Credit Constraints," 2002 Conference, April 22-23, 2002, St. Louis, Missouri 19072, NCR-134 Conference on Applied Commodity Price Analysis, Forecasting, and Market Risk Management.
    7. Zhao, Jieyuan & Goodwin, Barry K., 2012. "Dynamic Cross-Hedge Ratios: An Application of Copula Models," 2012 Annual Meeting, August 12-14, 2012, Seattle, Washington 124610, Agricultural and Applied Economics Association.
    8. Jonathan Dark, 2004. "Long term hedging of the Australian All Ordinaries Index using a bivariate error correction FIGARCH model," Monash Econometrics and Business Statistics Working Papers 7/04, Monash University, Department of Econometrics and Business Statistics.
    9. Sigbjørn Tveterås & Ragnar Tveterås, 2010. "The Global Competition for Wild Fish Resources between Livestock and Aquaculture," Journal of Agricultural Economics, Wiley Blackwell, vol. 61(2), pages 381-397.

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