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The cyclical behaviour of commodities

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  • Marcelo Pereira
  • Sofia B. Ramos
  • José G. Dias

Abstract

Commodities are known to exhibit cyclical behaviour. This paper studies the dynamics of commodities regimes and their implications for portfolio diversification. Using an extension of the regime-switching model, we find that the 12 commodities studied can be clustered into four groups with different regime dynamics, demonstrating that the asset class behaviour of commodities is far from homogeneous. The existence of two regimes is transversal to the assets studied. One regime is marked by high volatility and the other by low volatility. In both regimes, most of the commodities exhibit returns that are not statistically significantly different from those of the stock market regime. The exceptions are oil and natural gas during the low-volatility regime. The analysis of regime synchronization shows that our stock market proxy has low synchronization with commodities, which suggests potential diversification value from adding commodities to an equity portfolio. Based on portfolio optimization, we find that commodities are included in the optimal portfolios in the bull and bear regime of the Standard & Poor’s 500 index. The benefits of diversifying into commodities are particularly strong in the bear stock market regime.

Suggested Citation

  • Marcelo Pereira & Sofia B. Ramos & José G. Dias, 2017. "The cyclical behaviour of commodities," The European Journal of Finance, Taylor & Francis Journals, vol. 23(12), pages 1107-1128, September.
    • Handle: RePEc:taf:eurjfi:v:23:y:2017:i:12:p:1107-1128
    DOI: 10.1080/1351847X.2016.1205505
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    Cited by:

    1. Yip, Pick Schen & Brooks, Robert & Do, Hung Xuan & Nguyen, Duc Khuong, 2020. "Dynamic volatility spillover effects between oil and agricultural products," International Review of Financial Analysis, Elsevier, vol. 69(C).
    2. Dony Abdul Chalid & Rangga Handika, 2022. "Comovement and contagion in commodity markets," Cogent Economics & Finance, Taylor & Francis Journals, vol. 10(1), pages 2064079-206, December.
    3. Hernandez, Jose Arreola & Shahzad, Syed Jawad Hussain & Sadorsky, Perry & Uddin, Gazi Salah & Bouri, Elie & Kang, Sang Hoon, 2022. "Regime specific spillovers across US sectors and the role of oil price volatility," Energy Economics, Elsevier, vol. 107(C).
    4. Nicholas Apergis & Tasawar Hayat & Tareq Saeed, 2021. "Cyclicality of commodity markets with respect to the U.S. economic policy uncertainty based on granger causality in quantiles," Economic Notes, Banca Monte dei Paschi di Siena SpA, vol. 50(1), February.
    5. Constandina Koki & Stefanos Leonardos & Georgios Piliouras, 2020. "Exploring the Predictability of Cryptocurrencies via Bayesian Hidden Markov Models," Papers 2011.03741, arXiv.org, revised Dec 2020.
    6. Koki, Constandina & Leonardos, Stefanos & Piliouras, Georgios, 2022. "Exploring the predictability of cryptocurrencies via Bayesian hidden Markov models," Research in International Business and Finance, Elsevier, vol. 59(C).

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