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Mixed-frequency Drivers of Precious Metal Prices

Author

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  • Matěj Liberda

    (Department of Finance, Faculty of Economics and Administration, Masaryk University in Brno, Žerotínovo nám. 617/9, 601 77, Brno, Czech Republic)

Abstract
Lack of intrinsic value, hybrid nature of commodities and recent financialization of commodity markets make of understanding precious metals price moves complicated. Predicting future development of precious metals market can be more feasible if we discover what drives these markets and describe nature of the drivers. The aim of the paper is to explain metal price movements by assessing an impact of multiple economic and financial factors. Based on the literature review we study 8 possible macroeconomic and financial drivers. The data are collected from Bloomberg. We use mixed-data-sampling methodology that enables me to study drivers of various frequencies (daily and monthly) simultaneously in a single model. Results show that the interest rate, the exchange rate, stock levels, stock index returns and crude oil returns are generally significant to drive precious metal markets. The stock index has the most significant impact on the metals returns that is negative. Furthermore, the results divide precious metals into two groups with gold and silver on the one hand and platinum and palladium on the other. The first group is worse explained by considered drivers. Moreover, the interest rate does not have any impact on the price development of gold and silver and crude oil returns influence the pair negatively, contrary to the second pair of platinum and palladium.

Suggested Citation

  • Matěj Liberda, 2017. "Mixed-frequency Drivers of Precious Metal Prices," Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, Mendel University Press, vol. 65(6), pages 2007-2015.
    • Handle: RePEc:mup:actaun:actaun_2017065062007
    DOI: 10.11118/actaun201765062007
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    References listed on IDEAS

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    1. Büyükşahin, Bahattin & Robe, Michel A., 2014. "Speculators, commodities and cross-market linkages," Journal of International Money and Finance, Elsevier, vol. 42(C), pages 38-70.
    2. Hammoudeh, Shawkat M. & Yuan, Yuan & McAleer, Michael & Thompson, Mark A., 2010. "Precious metals-exchange rate volatility transmissions and hedging strategies," International Review of Economics & Finance, Elsevier, vol. 19(4), pages 633-647, October.
    3. Clayton,Blake C., 2016. "Commodity Markets and the Global Economy," Cambridge Books, Cambridge University Press, number 9781107042513, September.
    4. Jeffrey A Frankel & Andrew K Rose, 2010. "Determinants of Agricultural and Mineral Commodity Prices," RBA Annual Conference Volume (Discontinued), in: Renée Fry & Callum Jones & Christopher Kent (ed.),Inflation in an Era of Relative Price Shocks, Reserve Bank of Australia.
    5. Kagraoka, Yusho, 2016. "Common dynamic factors in driving commodity prices: Implications of a generalized dynamic factor model," Economic Modelling, Elsevier, vol. 52(PB), pages 609-617.
    6. Gary B. Gorton & Fumio Hayashi & K. Geert Rouwenhorst, 2013. "The Fundamentals of Commodity Futures Returns," Review of Finance, European Finance Association, vol. 17(1), pages 35-105.
    7. Ghysels, Eric & Santa-Clara, Pedro & Valkanov, Rossen, 2004. "The MIDAS Touch: Mixed Data Sampling Regression Models," University of California at Los Angeles, Anderson Graduate School of Management qt9mf223rs, Anderson Graduate School of Management, UCLA.
    8. Zhang, Yue-Jun & Wei, Yi-Ming, 2010. "The crude oil market and the gold market: Evidence for cointegration, causality and price discovery," Resources Policy, Elsevier, vol. 35(3), pages 168-177, September.
    9. Sari, Ramazan & Hammoudeh, Shawkat & Soytas, Ugur, 2010. "Dynamics of oil price, precious metal prices, and exchange rate," Energy Economics, Elsevier, vol. 32(2), pages 351-362, March.
    10. Andreou, Elena & Ghysels, Eric & Kourtellos, Andros, 2010. "Regression models with mixed sampling frequencies," Journal of Econometrics, Elsevier, vol. 158(2), pages 246-261, October.
    11. Frankel, Jeffrey A., 2014. "Effects of speculation and interest rates in a “carry trade” model of commodity prices," Journal of International Money and Finance, Elsevier, vol. 42(C), pages 88-112.
    12. Ghysels, Eric & Santa-Clara, Pedro & Valkanov, Rossen, 2006. "Predicting volatility: getting the most out of return data sampled at different frequencies," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 59-95.
    13. Śmiech, Sławomir & Papież, Monika & Dąbrowski, Marek A., 2015. "Does the euro area macroeconomy affect global commodity prices? Evidence from a SVAR approach," International Review of Economics & Finance, Elsevier, vol. 39(C), pages 485-503.
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    Cited by:

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    2. Schischke, A. & Papenfuß, P. & Brem, M. & Kurz, P. & Rathgeber, A.W., 2023. "Sustainable energy transition and its demand for scarce resources: Insights into the German Energiewende through a new risk assessment framework," Renewable and Sustainable Energy Reviews, Elsevier, vol. 176(C).

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