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Influence in commodity markets: Measuring co‐movement globally

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  • Fernandez, Viviana
Abstract
This article focuses on a new measure of global co-movement defined as influence: the average partial correlation of one asset with respect to others. The influence of nominal returns and real price cycles of various commodities is computed for the period of January 1968–December 2013. The estimation results show that there is strong co-movement among the average influences of nominal returns of industrial and precious metals since 2003. From an investor׳s perspective, this suggests a reduction in the benefits of portfolio diversification and a convergence towards a single metal class. On the other hand, and as expected, average influence among unrelated commodity returns is generally negligible, except for the period of financial turmoil of 2007–2010. By contrast the influence of real price cycles tends to be highly significant over the whole sample period, even among unrelated commodities. These findings indicate that economic agents׳ perceiving all commodities as a sole asset class is essentially a short-term phenomenon linked to business cycles. Two extensions of this framework are discussed: macroeconomic determinants of commodity influence and portfolio investment decisions.

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  • Fernandez, Viviana, 2015. "Influence in commodity markets: Measuring co‐movement globally," Resources Policy, Elsevier, vol. 45(C), pages 151-164.
    • Handle: RePEc:eee:jrpoli:v:45:y:2015:i:c:p:151-164
    DOI: 10.1016/j.resourpol.2015.04.008
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    Cited by:

    1. Sipan Aslan & Ceylan Yozgatligil & Cem Iyigun, 2018. "Temporal clustering of time series via threshold autoregressive models: application to commodity prices," Annals of Operations Research, Springer, vol. 260(1), pages 51-77, January.
    2. Liu, Chang & Sun, Xiaolei & Wang, Jun & Li, Jianping & Chen, Jianming, 2021. "Multiscale information transmission between commodity markets: An EMD-Based transfer entropy network," Research in International Business and Finance, Elsevier, vol. 55(C).
    3. Lucey, Brian M. & Vigne, Samuel A. & Ballester, Laura & Barbopoulos, Leonidas & Brzeszczynski, Janusz & Carchano, Oscar & Dimic, Nebojsa & Fernandez, Viviana & Gogolin, Fabian & González-Urteaga, Ana , 2018. "Future directions in international financial integration research - A crowdsourced perspective," International Review of Financial Analysis, Elsevier, vol. 55(C), pages 35-49.
    4. Marañon, Matias & Kumral, Mustafa, 2019. "Kondratiev long cycles in metal commodity prices," Resources Policy, Elsevier, vol. 61(C), pages 21-28.
    5. Lin, Min & Wang, Gang-Jin & Xie, Chi & Stanley, H. Eugene, 2018. "Cross-correlations and influence in world gold markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 504-512.
    6. Wadud, Sania & Gronwald, Marc & Durand, Robert B. & Lee, Seungho, 2023. "Co-movement between commodity and equity markets revisited—An application of the Thick Pen method," International Review of Financial Analysis, Elsevier, vol. 87(C).
    7. Naeem, Muhammad Abubakr & Hasan, Mudassar & Arif, Muhammad & Suleman, Muhammad Tahir & Kang, Sang Hoon, 2022. "Oil and gold as a hedge and safe-haven for metals and agricultural commodities with portfolio implications," Energy Economics, Elsevier, vol. 105(C).
    8. Cunado, Juncal & Gil-Alana, Luis A. & Gupta, Rangan, 2019. "Persistence in trends and cycles of gold and silver prices: Evidence from historical data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514(C), pages 345-354.
    9. Bahloul, Walid & Balcilar, Mehmet & Cunado, Juncal & Gupta, Rangan, 2018. "The role of economic and financial uncertainties in predicting commodity futures returns and volatility: Evidence from a nonparametric causality-in-quantiles test," Journal of Multinational Financial Management, Elsevier, vol. 45(C), pages 52-71.
    10. Antonakakis, Nikolaos & Chang, Tsangyao & Cunado, Juncal & Gupta, Rangan, 2018. "The relationship between commodity markets and commodity mutual funds: A wavelet-based analysis," Finance Research Letters, Elsevier, vol. 24(C), pages 1-9.
    11. Rafiq, Shuddhasattwa & Bloch, Harry, 2016. "Explaining commodity prices through asymmetric oil shocks: Evidence from nonlinear models," Resources Policy, Elsevier, vol. 50(C), pages 34-48.
    12. Chen, Peng & He, Limin & Yang, Xuan, 2021. "On interdependence structure of China's commodity market," Resources Policy, Elsevier, vol. 74(C).

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    More about this item

    Keywords

    Commodity markets; Conditional correlation; Portfolio investment;
    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
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • O13 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Agriculture; Natural Resources; Environment; Other Primary Products

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