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Commodities Inventory Effect

Author

Listed:
  • Jean-François Carpantier

    (CREA, Université du Luxembourg)

  • Arnaud Dufays

    (Université Catholique de Louvain)

Abstract

Does commodity price volatility increase when inventories are low? We are the first ones to document this relationship. To that aim, we estimate asym- metric volatility models for a large set of commodities over 1994-2011. Since inventories are hard to measure, especially for high frequency data, we use positive return shocks as a new original proxy for inventories and find that asymmetric GARCH models reveal a significant inventory effect for many commodities. The results look robust. They hold if we allow the uncondi- tional variance to vary over time and if we relax the parametric form.

Suggested Citation

  • Jean-François Carpantier & Arnaud Dufays, 2013. "Commodities Inventory Effect," DEM Discussion Paper Series 13-07, Department of Economics at the University of Luxembourg.
    • Handle: RePEc:luc:wpaper:13-07
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    Citations

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    Cited by:

    1. PIERRET, Diane, 2013. "The systemic risk of energy markets," LIDAM Discussion Papers CORE 2013018, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    2. Jean‐Francois Carpantier & Besik Samkharadze, 2013. "The Asymmetric Commodity Inventory Effect on the Optimal Hedge Ratio," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 33(9), pages 868-888, September.
    3. BAUWENS, Luc & HAFNER, Christian & LAURENT, Sébastien, 2011. "Volatility models," LIDAM Discussion Papers CORE 2011058, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
      • Bauwens, L. & Hafner C. & Laurent, S., 2011. "Volatility Models," LIDAM Discussion Papers ISBA 2011044, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
      • Bauwens, L. & Hafner, C. & Laurent, S., 2012. "Volatility Models," LIDAM Reprints ISBA 2012028, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    4. Sercan Demiralay & Selcuk Bayraci & H. Gaye Gencer, 2019. "Time-varying diversification benefits of commodity futures," Empirical Economics, Springer, vol. 56(6), pages 1823-1853, June.
    5. Ahmadi, Maryam & Bashiri Behmiri, Niaz & Manera, Matteo, 2016. "How is volatility in commodity markets linked to oil price shocks?," Energy Economics, Elsevier, vol. 59(C), pages 11-23.
    6. Behmiri, Niaz Bashiri & Manera, Matteo, 2015. "The role of outliers and oil price shocks on volatility of metal prices," Resources Policy, Elsevier, vol. 46(P2), pages 139-150.
    7. Svetlana Borovkova & Diego Mahakena, 2015. "News, volatility and jumps: the case of natural gas futures," Quantitative Finance, Taylor & Francis Journals, vol. 15(7), pages 1217-1242, July.
    8. Paraschiv, Florentina & Mudry, Pierre-Antoine & Andries, Alin Marius, 2015. "Stress-testing for portfolios of commodity futures," Economic Modelling, Elsevier, vol. 50(C), pages 9-18.

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    More about this item

    Keywords

    Asymmetries; Commodities; Inventory; Spline GARCH; VaR.;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
    • Q14 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Agricultural Finance

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