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Predicting Agri-Commodity Prices: an Asset Pricing Approach

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  • Yu-chin Chen

    (University of Washington)

  • Kenneth Rogoff

    (Harvard University)

  • Barbara Rossi

    (Duke University)

Abstract

Volatile and rising agricultural prices place significant strain on the global fight against poverty. An accurate reading of future food price movements would thus be an invaluable budgetary planning tool for government agencies and food aid programs aimed at alleviating hunger. Using the asset-pricing approach developed in Chen, Rogoff and Rossi (2010), we show that information from the currency and equity markets of several commodity-exporting economies can offer powerful help in forecasting world agricultural prices. Our formulation builds upon the notion that because these countries' currency and equity valuations depend on the world price of their commodity exports, market participants would incorporate expected future commodity price movements into the current values of these assets. As the exchange rate and equity markets are typically much more fluid than the agri-commodity markets (where prices tend to be more constrained by current supply and demand conditions), these asset prices can signal future agricultural price dynamics beyond information contained in the agri-commodity prices themselves. Our findings complement forecast methods based on structural factors such as supply, demand, and storage considerations.

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Bibliographic Info

Paper provided by University of Washington, Department of Economics in its series Working Papers with number UWEC-2010-02.

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Date of creation: Mar 2009
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Handle: RePEc:udb:wpaper:uwec-2010-02

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  1. Todd E. Clark & Michael W. McCracken, 2000. "Tests of Equal Forecast Accuracy and Encompassing for Nested Models," Econometric Society World Congress 2000 Contributed Papers 0319, Econometric Society.
  2. Craig Sugden, 2009. "Responding to High Commodity Prices," Asian-Pacific Economic Literature, Asia Pacific School of Economics and Government, The Australian National University, vol. 23(1), pages 79-105, 05.
  3. Donald W.K. Andrews, 1990. "Tests for Parameter Instability and Structural Change with Unknown Change Point," Cowles Foundation Discussion Papers 943, Cowles Foundation for Research in Economics, Yale University.
  4. C. Peter Timmer & David Dawe, 2007. "Managing Food Price Instability in Asia: A Macro Food Security Perspective ," Asian Economic Journal, East Asian Economic Association, vol. 21(1), pages 1-18, 03.
  5. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 33(1), pages 125-132.
  6. Charles Engel & Kenneth D. West, 2004. "Exchange Rates and Fundamentals," NBER Working Papers 10723, National Bureau of Economic Research, Inc.
  7. Chen, Yu-chin & Rogoff, Kenneth, 2003. "Commodity currencies," Journal of International Economics, Elsevier, vol. 60(1), pages 133-160, May.
  8. John Y. Campbell & Robert J. Shiller, 1986. "Cointegration and Tests of Present Value Models," Cowles Foundation Discussion Papers 785, Cowles Foundation for Research in Economics, Yale University.
  9. Chen, Yu-chin & Rogoff, Kenneth & Rossi, Barbara, 2008. "Can Exchange Rates Forecast Commodity Prices?," Working Papers 08-03, Duke University, Department of Economics.
  10. Rossi, Barbara, 2002. "Optimal Tests for Nested Model Selection with Underlying Parameter Instability," Working Papers 02-05, Duke University, Department of Economics.
  11. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-44, January.
  12. Todd E. Clark & Kenneth D. West, 2004. "Using out-of-sample mean squared prediction errors to test the Martingale difference hypothesis," Research Working Paper RWP 04-03, Federal Reserve Bank of Kansas City.
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Cited by:
  1. Kieran Burgess & Nicholas Rohde, 2013. "Can Exchange Rates Forecast Commodity Prices? Recent Evidence using Australian Data," Economics Bulletin, AccessEcon, vol. 33(1), pages 511-518.

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