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Commodity price movements in a general equilibrium model of storage

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Abstract

We embed the canonical rational expectations competitive storage model into a general equilibrium framework thereby allowing the non-linear commodity price dynamics implied by the competitive storage model to interact with the broader macroeconomy. Our main result is that the endogenous movement in interest rates implied under general equilibrium enhances the effects of competitive storage on commodity prices. Compared to a model in which the real interest rate is fixed, we find that storage in general equilibrium leads to more persistence in commodity prices and somewhat lower volatility. Moreover, the frequency of stockouts is lower in general equilibrium. A key mechanism driving this result is a link between the ability of the household to smooth consumption over time and the level of storage in the stochasic equilibrium. Finally, the model is used to examine the macroeconomic effects of both biofuel subsidies for ethanol producers and, separately, subsidies designed to insulate households from high food prices.

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  • David M. Arseneau & Sylvain Leduc, 2012. "Commodity price movements in a general equilibrium model of storage," International Finance Discussion Papers 1054, Board of Governors of the Federal Reserve System (U.S.).
    • Handle: RePEc:fip:fedgif:1054
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    Cited by:

    1. Kilian, Lutz & Lee, Thomas K., 2014. "Quantifying the speculative component in the real price of oil: The role of global oil inventories," Journal of International Money and Finance, Elsevier, vol. 42(C), pages 71-87.
    2. Hirbod Assa & Amal Dabbous & Nikolay Gospodinov, 2013. "A staggered pricing approach to modeling speculative storage: implications for commodity price dynamics," FRB Atlanta Working Paper 2013-08, Federal Reserve Bank of Atlanta.
    3. Jiang, Yonghong & Jiang, Cheng & Nie, He & Mo, Bin, 2019. "The time-varying linkages between global oil market and China's commodity sectors: Evidence from DCC-GJR-GARCH analyses," Energy, Elsevier, vol. 166(C), pages 577-586.
    4. Sylvain Leduc & Kevin Moran & Robert J. Vigfusson, 2023. "Learning in the Oil Futures Markets: Evidence and Macroeconomic Implications," The Review of Economics and Statistics, MIT Press, vol. 105(2), pages 392-407, March.
    5. Karimi, Nader & Salavati, Erfan & Assa, Hirbod & Adibi, Hojatollah, 2024. "A stochastic optimal stopping model for storable commodity prices," Statistics & Probability Letters, Elsevier, vol. 204(C).
    6. Conny Olovsson, 2019. "Oil prices in a general equilibrium model with precautionary demand for oil," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 32, pages 1-17, April.
    7. ap Gwilym, Rhys & Ebrahim, M. Shahid & El Alaoui, Abdelkader O. & Rahman, Hamid & Taamouti, Abderrahim, 2020. "Financial frictions and the futures pricing puzzle," Economic Modelling, Elsevier, vol. 87(C), pages 358-371.
    8. Gruber, Joseph W. & Vigfusson, Robert J., 2018. "Interest Rates And The Volatility And Correlation Of Commodity Prices," Macroeconomic Dynamics, Cambridge University Press, vol. 22(3), pages 600-619, April.
    9. Branger, Nicole & Grüning, Patrick & Schlag, Christian, 2016. "Commodities, financialization, and heterogeneous agents," SAFE Working Paper Series 131 [rev.], Leibniz Institute for Financial Research SAFE, revised 2016.
    10. Steven D. Baker, 2021. "The Financialization of Storable Commodities," Management Science, INFORMS, vol. 67(1), pages 471-499, January.
    11. Juan Antonio Galán-Gutiérrez & Rodrigo Martín-García, 2022. "Fundamentals vs. Financialization during Extreme Events: From Backwardation to Contango, a Copper Market Analysis during the COVID-19 Pandemic," Mathematics, MDPI, vol. 10(4), pages 1-23, February.
    12. Luis Catão & Roberto Chang, 2013. "Monetary Rules for Commodity Traders," IMF Economic Review, Palgrave Macmillan;International Monetary Fund, vol. 61(1), pages 52-91, April.
    13. Kleppe, Tore Selland & Oglend, Atle, 2017. "Estimating the competitive storage model: A simulated likelihood approach," Econometrics and Statistics, Elsevier, vol. 4(C), pages 39-56.
    14. Nader Karimi & Hirbod Assa & Erfan Salavati & Hojatollah Adibi, 2023. "Calibration of Storage Model by Multi-Stage Statistical and Machine Learning Methods," Computational Economics, Springer;Society for Computational Economics, vol. 62(4), pages 1437-1455, December.
    15. Pfuderer, Simone, 2014. "Are stockholders rational? An experimental approach to testing the competitive storage model," 88th Annual Conference, April 9-11, 2014, AgroParisTech, Paris, France 170537, Agricultural Economics Society.
    16. Vincent Bodart & François Courtoy & Erica Perego, 2021. "World Interest Rates and Macroeconomic Adjustments in Developing Commodity Producing Countries," Working Papers 2021-01, CEPII research center.
    17. Jin, Xin, 2019. "The role of market expectations in commodity price dynamics: Evidence from oil data," Journal of International Money and Finance, Elsevier, vol. 90(C), pages 1-18.
    18. Andrew Filardo & Jacopo Lombardi & Carlos Montoro, 2018. "Monetary policy spillovers, global commodity prices and cooperation," BIS Working Papers 696, Bank for International Settlements.
    19. Ginn, William & Pourroy, Marc, 2019. "Optimal monetary policy in the presence of food price subsidies," Economic Modelling, Elsevier, vol. 81(C), pages 551-575.
    20. Tore S. Kleppe & Atle Oglend, 2019. "Can limits‐to‐arbitrage from bounded storage improve commodity term‐structure modeling?," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 39(7), pages 865-889, July.
    21. Lutz Kilian, 2014. "Oil Price Shocks: Causes and Consequences," Annual Review of Resource Economics, Annual Reviews, vol. 6(1), pages 133-154, October.
    22. Gal Hochman & Scott Kaplan & Deepak Rajagopal & David Zilberman, 2012. "Biofuel and Food-Commodity Prices," Agriculture, MDPI, vol. 2(3), pages 1-10, September.
    23. Vorotnikova, Ekaterina, 2016. "Optimal Storage Capacity Allocation in Grain Merchandizing," 2016 Annual Meeting, February 6-9, 2016, San Antonio, Texas 230128, Southern Agricultural Economics Association.

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