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Some facts on the platinum-group elements

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  • Fernandez, Viviana

Abstract

The platinum-group elements (PGE) include platinum, palladium, rhodium, ruthenium, iridium, and osmium. In this article, we concentrate on the dependency structure and economic determinants of PGE, silver, and gold prices. We find that the strongest relationship is between silver and gold returns at a weekly frequency (July 1992–July 2016), which display tail dependence in bearish and bullish markets. By contrast, palladium and platinum display tail dependence with silver only under bearish markets. When focusing on real prices, at an annual horizon (1930–2014) the first principal component of PGE and silver prices is positively and strongly correlated with PGE/silver world production and US PGE/silver apparent consumption. At a monthly frequency (July 1992–July 2016) in turn, the first principal components of gold, silver, and PGE are positively and strongly associated with US industrial production, South Africa manufacturing production, and the US M1 and M2 monetary aggregates; and, to a lesser extent, inversely correlated with consumer sentiment and a trade-weighted US dollar index. To our knowledge, this is the first comprehensive study on PGE dependency with respect to other precious metals, such as silver and gold, and on PGE-price drivers.

Suggested Citation

  • Fernandez, Viviana, 2017. "Some facts on the platinum-group elements," International Review of Financial Analysis, Elsevier, vol. 52(C), pages 333-347.
  • Handle: RePEc:eee:finana:v:52:y:2017:i:c:p:333-347
    DOI: 10.1016/j.irfa.2017.04.003
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    References listed on IDEAS

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    1. Aruga, Kentaka & Managi, Shunsuke, 2011. "Testing the international linkage in the platinum-group metal futures markets," Resources Policy, Elsevier, vol. 36(4), pages 339-345.
    2. Krogscheepers, Corris & Gossel, Sean Joss, 2015. "Input cost and international demand effects on the production of platinum group metals in South Africa," Resources Policy, Elsevier, vol. 45(C), pages 193-201.
    3. Aas, Kjersti & Czado, Claudia & Frigessi, Arnoldo & Bakken, Henrik, 2009. "Pair-copula constructions of multiple dependence," Insurance: Mathematics and Economics, Elsevier, vol. 44(2), pages 182-198, April.
    4. Batten, Jonathan A. & Ciner, Cetin & Lucey, Brian M., 2010. "The macroeconomic determinants of volatility in precious metals markets," Resources Policy, Elsevier, vol. 35(2), pages 65-71, June.
    5. Brechmann, Eike Christian & Schepsmeier, Ulf, 2013. "Modeling Dependence with C- and D-Vine Copulas: The R Package CDVine," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 52(i03).
    6. Gleich, Benedikt & Achzet, Benjamin & Mayer, Herbert & Rathgeber, Andreas, 2013. "An empirical approach to determine specific weights of driving factors for the price of commodities—A contribution to the measurement of the economic scarcity of minerals and metals," Resources Policy, Elsevier, vol. 38(3), pages 350-362.
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    Cited by:

    1. Bao, Dun, 2020. "Dynamics and correlation of platinum-group metals spot prices," Resources Policy, Elsevier, vol. 68(C).
    2. 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.
    3. Vigne, Samuel A. & Lucey, Brian M. & O’Connor, Fergal A. & Yarovaya, Larisa, 2017. "The financial economics of white precious metals — A survey," International Review of Financial Analysis, Elsevier, vol. 52(C), pages 292-308.
    4. Salisu, Afees A. & Ndako, Umar B. & Oloko, Tirimisiyu F., 2019. "Assessing the inflation hedging of gold and palladium in OECD countries," Resources Policy, Elsevier, vol. 62(C), pages 357-377.

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    More about this item

    Keywords

    Platinum-group elements (PGE); Vine copula; Principal components;
    All these keywords.

    JEL classification:

    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • Q02 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General - - - Commodity Market

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