IDEAS home Printed from https://ideas.repec.org/a/spr/minecn/v37y2024i1d10.1007_s13563-023-00386-y.html
   My bibliography  Save this article

A macroeconomic viewpoint using a structural VAR analysis of silver price behaviour

Author

Listed:
  • Z. Robinson

    (University of South Africa)

Abstract

This article investigates silver price as a fluctuating commodity price since the financial crisis of 2007–2009. In this regard, a structural vector autoregression (VAR) was applied to observe the sensitivity of the silver price and future pricing due to changes in macroeconomic variables and to review changes in macroeconomic variables due to changes in the silver price. The main results show that the silver price is susceptible to changes in the gold price, increasing sideways. A shock to OECD GDP caused the silver price to increase which makes logical sense, thus showing a positive correlation between output and the silver price. A shock to the oil price caused the silver price to spike over the short term, then move sideways over the long term. A shock to the US Federal funds rate caused the silver price to dip over the short term, then increase slightly over the medium and move sideways over the long term, while a shock to the real effective exchange rate of the USA caused the silver price to increase sideways. The article sheds some light on the reactive status of the silver price to macroeconomic variables and its influence as a safe haven commodity.

Suggested Citation

  • Z. Robinson, 2024. "A macroeconomic viewpoint using a structural VAR analysis of silver price behaviour," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 37(1), pages 15-23, March.
  • Handle: RePEc:spr:minecn:v:37:y:2024:i:1:d:10.1007_s13563-023-00386-y
    DOI: 10.1007/s13563-023-00386-y
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13563-023-00386-y
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s13563-023-00386-y?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 look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Joëts, Marc & Mignon, Valérie & Razafindrabe, Tovonony, 2017. "Does the volatility of commodity prices reflect macroeconomic uncertainty?," Energy Economics, Elsevier, vol. 68(C), pages 313-326.
    2. Amare Wubishet Ayele & Emmanuel Gabreyohannes & Hayimro Edmealem, 2020. "Generalized Autoregressive Conditional Heteroskedastic Model to Examine Silver Price Volatility and Its Macroeconomic Determinant in Ethiopia Market," Journal of Probability and Statistics, Hindawi, vol. 2020, pages 1-10, May.
    3. Elie Bouri & Naji Jalkh, 2019. "Conditional quantiles and tail dependence in the volatilities of gold and silver," International Economics, CEPII research center, issue 157, pages 117-133.
    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. Issler, João Victor & Rodrigues, Claudia & Burjack, Rafael, 2014. "Using common features to understand the behavior of metal-commodity prices and forecast them at different horizons," Journal of International Money and Finance, Elsevier, vol. 42(C), pages 310-335.
    6. Zhu, Huiming & Peng, Cheng & You, Wanhai, 2016. "Quantile behaviour of cointegration between silver and gold prices," Finance Research Letters, Elsevier, vol. 19(C), pages 119-125.
    7. Joëts, Marc & Mignon, Valérie & Razafindrabe, Tovonony, 2017. "Does the volatility of commodity prices reflect macroeconomic uncertainty?," Energy Economics, Elsevier, vol. 68(C), pages 313-326.
    8. Zurika Robinson, 2019. "Revisiting gold price behaviour: a structural VAR," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 32(3), pages 365-372, November.
    Full references (including those not matched with items on IDEAS)

    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. Liu, Yang & Han, Liyan & Xu, Yang, 2021. "The impact of geopolitical uncertainty on energy volatility," International Review of Financial Analysis, Elsevier, vol. 75(C).
    2. Zuzanna Karolak, 2021. "Energy prices forecasting using nonlinear univariate models," Bank i Kredyt, Narodowy Bank Polski, vol. 52(6), pages 577-598.
    3. Awasthi, Kritika & Ahmad, Wasim & Rahman, Abdul & Phani, B.V., 2020. "When US sneezes, clichés spread: How do the commodity index funds react then?," Resources Policy, Elsevier, vol. 69(C).
    4. Hu, Min & Zhang, Dayong & Ji, Qiang & Wei, Lijian, 2020. "Macro factors and the realized volatility of commodities: A dynamic network analysis," Resources Policy, Elsevier, vol. 68(C).
    5. 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.
    6. Galán-Gutiérrez, Juan Antonio & Labeaga, José M. & Martín-García, Rodrigo, 2023. "Cointegration between high base metals prices and backwardation: Getting ready for the metals super-cycle," Resources Policy, Elsevier, vol. 81(C).
    7. Srivastava, Mrinalini & Rao, Amar & Parihar, Jaya Singh & Chavriya, Shubham & Singh, Surendar, 2023. "What do the AI methods tell us about predicting price volatility of key natural resources: Evidence from hyperparameter tuning," Resources Policy, Elsevier, vol. 80(C).
    8. Yang Liu & Liyan Han & Libo Yin, 2018. "Does news uncertainty matter for commodity futures markets? Heterogeneity in energy and non‐energy sectors," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 38(10), pages 1246-1261, October.
    9. Bouri, Elie & Lucey, Brian & Saeed, Tareq & Vo, Xuan Vinh, 2021. "The realized volatility of commodity futures: Interconnectedness and determinants#," International Review of Economics & Finance, Elsevier, vol. 73(C), pages 139-151.
    10. Zhou, Liyun & Huang, Jialiang, 2020. "Contagion of future-level sentiment in Chinese Agricultural Futures Markets," Pacific-Basin Finance Journal, Elsevier, vol. 61(C).
    11. Gilles Dufrénot & William Ginn & Marc Pourroy, 2023. "ENSO Climate Patterns on Global Economic Conditions," AMSE Working Papers 2308, Aix-Marseille School of Economics, France.
    12. Fatemeh Salimi Namin, 2020. "Exchange Rates, Stock Prices, and Stock Market Uncertainty," AMSE Working Papers 2037, Aix-Marseille School of Economics, France.
    13. Pan, Zhiyuan & Xiao, Dongli & Dong, Qingma & Liu, Li, 2022. "Structural breaks, macroeconomic fundamentals and cross hedge ratio," Finance Research Letters, Elsevier, vol. 47(PA).
    14. Dudda, Tom L. & Klein, Tony & Nguyen, Duc Khuong & Walther, Thomas, 2022. "Common Drivers of Commodity Futures?," QBS Working Paper Series 2022/05, Queen's University Belfast, Queen's Business School.
    15. Ren, Yinghua & Tan, Anqi & Zhu, Huiming & Zhao, Wanru, 2022. "Does economic policy uncertainty drive nonlinear risk spillover in the commodity futures market?," International Review of Financial Analysis, Elsevier, vol. 81(C).
    16. Govorukha, Kristina & Mayer, Philip & Rübbelke, Dirk & Vögele, Stefan, 2020. "Economic disruptions in long-term energy scenarios – Implications for designing energy policy," Energy, Elsevier, vol. 212(C).
    17. Junguo Hua & Hui Li & Zejun He & Jing Ding & Futong Jin, 2022. "The Microcosmic Mechanism and Empirical Test of Uncertainty on the Non-Linear Fluctuation of Chinese Grain Prices-Based on the Perspective of Global Economic Policy Uncertainty," Agriculture, MDPI, vol. 12(10), pages 1-17, September.
    18. Yaya, OlaOluwa S. & Lukman, Adewale F. & Vo, Xuan Vinh, 2022. "Persistence and volatility spillovers of bitcoin price to gold and silver prices," Resources Policy, Elsevier, vol. 79(C).
    19. Rossen, Anja, 2015. "What are metal prices like? Co-movement, price cycles and long-run trends," Resources Policy, Elsevier, vol. 45(C), pages 255-276.
    20. Gong, Xu & Xu, Jun, 2022. "Geopolitical risk and dynamic connectedness between commodity markets," Energy Economics, Elsevier, vol. 110(C).

    More about this item

    Keywords

    Silver; Gold; Oil; Commodity prices;
    All these keywords.

    Statistics

    Access and download statistics

    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:spr:minecn:v:37:y:2024:i:1:d:10.1007_s13563-023-00386-y. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.