IDEAS home Printed from https://ideas.repec.org/a/mup/actaun/actaun_2017065062007.html
   My bibliography  Save this article

Mixed-frequency Drivers of Precious Metal Prices

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://acta.mendelu.cz/doi/10.11118/actaun201765062007.html
    Download Restriction: free of charge
    File URL: http://acta.mendelu.cz/doi/10.11118/actaun201765062007.pdf
    Download Restriction: free of charge
    File URL: https://libkey.io/10.11118/actaun201765062007?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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, October.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Dinh, Theu & Goutte, Stéphane & Nguyen, Duc Khuong & Walther, Thomas, 2022. "Economic drivers of volatility and correlation in precious metal markets," Journal of Commodity Markets, Elsevier, vol. 28(C).
    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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yao, Wei & Alexiou, Constantinos, 2024. "On the transmission mechanism between the inventory arbitrage activity, speculative activity and the commodity price under the US QE policy: Evidence from a TVP-VAR model," International Review of Economics & Finance, Elsevier, vol. 89(PA), pages 1054-1072.
    2. Degiannakis, Stavros & Filis, George, 2018. "Forecasting oil prices: High-frequency financial data are indeed useful," Energy Economics, Elsevier, vol. 76(C), pages 388-402.
    3. Degiannakis, Stavros & Filis, George, 2017. "Forecasting oil prices," MPRA Paper 77531, University Library of Munich, Germany.
    4. Wan, Jer-Yuh & Kao, Chung-Wei, 2015. "Interactions between oil and financial markets — Do conditions of financial stress matter?," Energy Economics, Elsevier, vol. 52(PA), pages 160-175.
    5. Koch, Nicolas, 2014. "Tail events: A new approach to understanding extreme energy commodity prices," Energy Economics, Elsevier, vol. 43(C), pages 195-205.
    6. Eric Ghysels & J. Isaac Miller, 2015. "Testing for Cointegration with Temporally Aggregated and Mixed-Frequency Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(6), pages 797-816, November.
    7. Stylianos Asimakopoulos & Joan Paredes & Thomas Warmedinger, 2020. "Real‐Time Fiscal Forecasting Using Mixed‐Frequency Data," Scandinavian Journal of Economics, Wiley Blackwell, vol. 122(1), pages 369-390, January.
    8. Bakas, Dimitrios & Triantafyllou, Athanasios, 2018. "The impact of uncertainty shocks on the volatility of commodity prices," Journal of International Money and Finance, Elsevier, vol. 87(C), pages 96-111.
    9. Gupta, Rangan & Majumdar, Anandamayee & Pierdzioch, Christian & Wohar, Mark E., 2017. "Do terror attacks predict gold returns? Evidence from a quantile-predictive-regression approach," The Quarterly Review of Economics and Finance, Elsevier, vol. 65(C), pages 276-284.
    10. Bao, Dun, 2020. "Dynamics and correlation of platinum-group metals spot prices," Resources Policy, Elsevier, vol. 68(C).
    11. Valadkhani, Abbas & Smyth, Russell, 2018. "Asymmetric responses in the timing, and magnitude, of changes in Australian monthly petrol prices to daily oil price changes," Energy Economics, Elsevier, vol. 69(C), pages 89-100.
    12. Barbaglia, Luca & Wilms, Ines & Croux, Christophe, 2016. "Commodity dynamics: A sparse multi-class approach," Energy Economics, Elsevier, vol. 60(C), pages 62-72.
    13. Kuzin, Vladimir & Marcellino, Massimiliano & Schumacher, Christian, 2011. "MIDAS vs. mixed-frequency VAR: Nowcasting GDP in the euro area," International Journal of Forecasting, Elsevier, vol. 27(2), pages 529-542.
    14. Ma, Richie Ruchuan & Xiong, Tao, 2021. "Price explosiveness in nonferrous metal futures markets," Economic Modelling, Elsevier, vol. 94(C), pages 75-90.
    15. Miller, J. Isaac, 2018. "Simple robust tests for the specification of high-frequency predictors of a low-frequency series," Econometrics and Statistics, Elsevier, vol. 5(C), pages 45-66.
    16. Hanan Naser, 2015. "Estimating and forecasting Bahrain quarterly GDP growth using simple regression and factor-based methods," Empirical Economics, Springer, vol. 49(2), pages 449-479, September.
    17. Nuttanan Wichitaksorn, 2020. "Analyzing and Forecasting Thai Macroeconomic Data using Mixed-Frequency Approach," PIER Discussion Papers 146, Puey Ungphakorn Institute for Economic Research.
    18. 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).
    19. 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.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:mup:actaun:actaun_2017065062007. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Ivo Andrle (email available below). General contact details of provider: https://mendelu.cz/en/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.