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Assessing efficiency and investment opportunities in commodities: A time series and portfolio simulations approach

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  • Jawadi, Fredj
  • Ftiti, Zied
  • Hdia, Mouna

Abstract

This paper investigates the informational efficiency hypothesis in the short and long term for four major commodity markets (oil, gas, electricity, and coal) from January 1997 to January 2016. Unlike previous studies, we provide a more concise comparative analysis by focusing on different classes of commodities for a large sample, including 5 developed and 3 emerging regions and covering 46 countries. We apply different parametric and non-parametric econometric tests. Our study provides two interesting findings. First, we show that commodity markets are informationally inefficient in the short term. Our portfolio simulations highlight that commodities might provide “good” investment opportunities, but those opportunities vary according to commodity class and regions. Second, we show that most commodity markets become informationally efficient in the long term, thereby reducing investors' interest for the duration. Thus, commodity markets might be used to hedge investor’s portfolios, particularly for speculators and chartists in the short term, while these investments might not be appealing in these markets in the long term.

Suggested Citation

  • Jawadi, Fredj & Ftiti, Zied & Hdia, Mouna, 2017. "Assessing efficiency and investment opportunities in commodities: A time series and portfolio simulations approach," Economic Modelling, Elsevier, vol. 64(C), pages 567-588.
    • Handle: RePEc:eee:ecmode:v:64:y:2017:i:c:p:567-588
    DOI: 10.1016/j.econmod.2017.04.021
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    Cited by:

    1. Wu, Bi-Bo, 2021. "The dynamics of oil on China’s commodity sectors: What can we learn from a quantile perspective?," Journal of Commodity Markets, Elsevier, vol. 23(C).
    2. 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.
    3. Bernardina Algieri & Matthias Kalkuhl, 2019. "Efficiency and Forecast Performance of Commodity Futures Markets," American Journal of Economics and Business Administration, Science Publications, vol. 11(1), pages 19-34, June.
    4. Charfeddine, Lanouar & Benlagha, Noureddine & Maouchi, Youcef, 2020. "Investigating the dynamic relationship between cryptocurrencies and conventional assets: Implications for financial investors," Economic Modelling, Elsevier, vol. 85(C), pages 198-217.
    5. You‐How Go & Wee‐Yeap Lau, 2023. "What do we know about informational efficiency? Three puzzles and the new direction forward," Journal of Economic Surveys, Wiley Blackwell, vol. 37(4), pages 1489-1525, September.
    6. Ghulam Mujtaba & Asima Siddique & Nader Naifar & Syed Jawad Hussain Shahzad, 2024. "Hedge and safe haven role of commodities for the US and Chinese equity markets," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 29(2), pages 2381-2414, April.
    7. Jebabli, Ikram & Roubaud, David, 2018. "Time-varying efficiency in food and energy markets: Evidence and implications," Economic Modelling, Elsevier, vol. 70(C), pages 97-114.

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    More about this item

    Keywords

    C10; G14; Informational efficiency; Commodity markets; Hedging; Portfolio simulations; Time series;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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