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Estimating the Competitive Storage Model with Trending Commodity Prices

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  • Christophe Gouel
  • Nicolas Legrand

Abstract

We present a method to estimate jointly the parameters of a standard commodity storage model and the parameters characterizing the trend in commodity prices. This procedure allows the influence of a possible trend to be removed without restricting the model specification, and allows model and trend selection based on statistical criteria. The trend is modeled deterministically using linear or cubic spline functions of time. The results show that storage models with trend are always preferred to models without trend. They yield more plausible estimates of the structural parameters, with storage costs and demand elasticities that are more consistent with the literature. They imply occasional stockouts, whereas without trend the estimated models predict no stockouts over the sample period for most commodities. Moreover, accounting for a trend in the estimation implies price moments closer to those observed in commodity prices. Our results support the empirical relevance of the speculative storage model, and show that storage model estimations should not neglect the possibility of long-run price trends.
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  • Christophe Gouel & Nicolas Legrand, 2017. "Estimating the Competitive Storage Model with Trending Commodity Prices," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(4), pages 744-763, June.
    • Handle: RePEc:wly:japmet:v:32:y:2017:i:4:p:744-763
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    Cited by:

    1. Winkelried, Diego, 2021. "Unit roots in real primary commodity prices? A meta-analysis of the Grilli and Yang data set," Journal of Commodity Markets, Elsevier, vol. 23(C).
    2. Nicolas Legrand, 2023. "“The empirical relevance of the competitive storage model” by Cafiero et al. (2011): Replication, robustness, and extension," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 45(3), pages 1493-1514, September.
    3. Guerra Vallejos, Ernesto & Bobenrieth Hochfarber, Eugenio & Bobenrieth Hochfarber, Juan & Wright, Brian D., 2021. "Solving dynamic stochastic models with multiple occasionally binding constraints," Economic Modelling, Elsevier, vol. 105(C).
    4. Chavas, Jean-Paul & Li, Jian, 2017. "The Effects of Private Stocks versus Public Stocks on Food Price Volatility," 2017 Annual Meeting, July 30-August 1, Chicago, Illinois 259185, Agricultural and Applied Economics Association.
    5. Assia Elgouacem, 2018. "Essays on investment and saving [Essais sur l’investissement et l’épargne]," SciencePo Working papers tel-03419405, HAL.
    6. V., Ernesto Guerra & H., Eugenio Bobenrieth & H., Juan Bobenrieth & Wright, Brian D., 2023. "Endogenous thresholds in energy prices: Modeling and empirical estimation," Energy Economics, Elsevier, vol. 121(C).
    7. Eugenio S.A. Bobenrieth & Juan R.A. Bobenrieth & Ernesto A. Guerra & Brian D. Wright & Di Zeng, 2021. "Putting the Empirical Commodity Storage Model Back on Track: Crucial Implications of a “Negligible” Trend," American Journal of Agricultural Economics, John Wiley & Sons, vol. 103(3), pages 1034-1057, May.
    8. Farhangdoost, Sara & Etienne, Xiaoli L., 2020. "Time-Varying Storage Announcement Effect in Natural Gas Market," 2020 Annual Meeting, July 26-28, Kansas City, Missouri 304476, Agricultural and Applied Economics Association.
    9. Tran, A. Nam & Welch, Jarrod R. & Lobell, David & Roberts, Michael J. & Schlenker, Wolfram, 2012. "Commodity Prices and Volatility in Response to Anticipated Climate Change," 2012 Annual Meeting, August 12-14, 2012, Seattle, Washington 124827, Agricultural and Applied Economics Association.
    10. Gouel, Christophe & Legrand, Nicolas, 2016. "Bayesian Estimation of the Storage Model using Information on Quantities," 2016 Annual Meeting, July 31-August 2, Boston, Massachusetts 235599, Agricultural and Applied Economics Association.
    11. Nicolas Legrand, 2019. "The Empirical Merit Of Structural Explanations Of Commodity Price Volatility: Review And Perspectives," Journal of Economic Surveys, Wiley Blackwell, vol. 33(2), pages 639-664, April.
    12. Christophe Gouel & Nicolas Legrand, 2022. "The Role of Storage in Commodity Markets: Indirect Inference Based on Grains Data," Working Papers 2022-04, CEPII research center.
    13. Jinniao Qiu & Antony Ware & Yang Yang, 2024. "Stochastic Path-Dependent Volatility Models for Price-Storage Dynamics in Natural Gas Markets and Discrete-Time Swing Option Pricing," Papers 2406.16400, arXiv.org.
    14. Fabio Gaetano Santeramo & Emilia Lamonaca & Francesco Contò & Gianluca Nardone & Antonio Stasi, 2018. "Drivers of grain price volatility: a cursory critical review," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 64(8), pages 347-356.
    15. Bobenrieth, Eugenio S. & Bobenrieth, Juan R.A. & Wright, Brian D. & Guerra, Ernesto A., 2022. "A Weak Latent Trend Hides Strong Price Predictability: An Empirical Method For An Unrecognized Problem," 2022 Annual Meeting, July 31-August 2, Anaheim, California 322210, Agricultural and Applied Economics Association.
    16. Juan R. A. Bobenrieth & Eugenio S. A. Bobenrieth & Andrés F. Villegas & Brian D. Wright, 2022. "Estimation of Endogenous Volatility Models with Exponential Trends," Mathematics, MDPI, vol. 10(15), pages 1-27, July.
    17. Rubaszek, Michał & Uddin, Gazi Salah, 2020. "The role of underground storage in the dynamics of the US natural gas market: A threshold model analysis," Energy Economics, Elsevier, vol. 87(C).
    18. Oglend, Atle, 2022. "The commodities/equities beta term-structure," Journal of Commodity Markets, Elsevier, vol. 28(C).
    19. Assia Elgouacem, 2018. "Essays on investment and saving [Essais sur l’investissement et l’épargne]," SciencePo Working papers Main tel-03419405, HAL.
    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. Kjartan Kloster Osmundsen & Tore Selland Kleppe & Roman Liesenfeld & Atle Oglend, 2021. "Estimating the Competitive Storage Model with Stochastic Trends in Commodity Prices," Econometrics, MDPI, vol. 9(4), pages 1-24, November.

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    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • Q11 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Aggregate Supply and Demand Analysis; Prices

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