IDEAS home Printed from https://ideas.repec.org/p/ces/ceswps/_12559.html

Long-Run Linkages and Parameter Instability in the Gold–Silver Relationship, 2010–2025

Author

Listed:
  • Guglielmo Maria Caporale
  • Antonio Fons Palomares
  • Luis Alberiko Gil-Alana

Abstract

This paper examines long-run linkages and possible instabilities in the gold–silver price relationship using daily futures prices over the period from 4 January 2010 to 28 November 2025. The empirical analysis includes unit-root and cointegration tests as well as endogenous structural break tests, namely the Pruned Exact Linear Time (PELT) algorithm applied to the Engle–Granger residuals and the Bai–Perron endogenous break test, both detecting a break in late 2017. Standard cointegration tests fail to detect a stable long-run equilibrium over the full sample and the pre-break subsample, while one is found in the post-break subsample. Further, the Local Whittle fractional integration method provides evidence of a high degree of persistence consistent with long-memory dynamics. The estimation of a Fractionally Cointegrated VAR (FCVAR) model corroborates the previous findings: although full-sample evidence for cointegration is limited, a stable and economically meaningful long-run relationship between gold and silver emerges clearly in the post-break period. The results are shown to be robust across frequencies.

Suggested Citation

  • Guglielmo Maria Caporale & Antonio Fons Palomares & Luis Alberiko Gil-Alana, 2026. "Long-Run Linkages and Parameter Instability in the Gold–Silver Relationship, 2010–2025," CESifo Working Paper Series 12559, CESifo.
    • Handle: RePEc:ces:ceswps:_12559
    as

    Download full text from publisher

    File URL: https://www.ifo.de/DocDL/cesifo1_wp12559.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Søren Johansen & Morten Ørregaard Nielsen, 2012. "Likelihood Inference for a Fractionally Cointegrated Vector Autoregressive Model," Econometrica, Econometric Society, vol. 80(6), pages 2667-2732, November.
    2. Whitney Newey & Kenneth West, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 33(1), pages 125-132.
    3. Dirk G. Baur & Brian M. Lucey, 2010. "Is Gold a Hedge or a Safe Haven? An Analysis of Stocks, Bonds and Gold," The Financial Review, Eastern Finance Association, vol. 45(2), pages 217-229, May.
    4. Gil-Alana, Luis A. & Aye, Goodness C. & Gupta, Rangan, 2015. "Trends and cycles in historical gold and silver prices," Journal of International Money and Finance, Elsevier, vol. 58(C), pages 98-109.
    5. Guglielmo Maria Caporale & Luis Alberiko Gil-Alana, 2023. "Gold and silver as safe havens: A fractional integration and cointegration analysis," PLOS ONE, Public Library of Science, vol. 18(3), pages 1-9, March.
    6. Robert Engle & Clive Granger, 2015. "Co-integration and error correction: Representation, estimation, and testing," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 39(3), pages 106-135.
    7. 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.
    8. Johansen, Soren, 1991. "Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregressive Models," Econometrica, Econometric Society, vol. 59(6), pages 1551-1580, November.
    9. Jushan Bai & Pierre Perron, 2003. "Computation and analysis of multiple structural change models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(1), pages 1-22.
    10. Schweikert, Karsten, 2018. "Are gold and silver cointegrated? New evidence from quantile cointegrating regressions," Journal of Banking & Finance, Elsevier, vol. 88(C), pages 44-51.
    11. Jushan Bai & Pierre Perron, 1998. "Estimating and Testing Linear Models with Multiple Structural Changes," Econometrica, Econometric Society, vol. 66(1), pages 47-78, January.
    12. Johansen, Søren & Nielsen, Morten Ørregaard, 2010. "Likelihood inference for a nonstationary fractional autoregressive model," Journal of Econometrics, Elsevier, vol. 158(1), pages 51-66, September.
    13. Johansen, Soren, 1988. "Statistical analysis of cointegration vectors," Journal of Economic Dynamics and Control, Elsevier, vol. 12(2-3), pages 231-254.
    14. Pindyck, Robert S & Rotemberg, Julio J, 1990. "The Excess Co-movement of Commodity Prices," Economic Journal, Royal Economic Society, vol. 100(403), pages 1173-1189, December.
    15. Hood, Matthew & Malik, Farooq, 2013. "Is gold the best hedge and a safe haven under changing stock market volatility?," Review of Financial Economics, Elsevier, vol. 22(2), pages 47-52.
    16. Matthew Hood & Farooq Malik, 2013. "Is gold the best hedge and a safe haven under changing stock market volatility?," Review of Financial Economics, John Wiley & Sons, vol. 22(2), pages 47-52, April.
    17. Baillie, Richard T., 1996. "Long memory processes and fractional integration in econometrics," Journal of Econometrics, Elsevier, vol. 73(1), pages 5-59, July.
    18. Baur, Dirk G. & McDermott, Thomas K., 2010. "Is gold a safe haven? International evidence," Journal of Banking & Finance, Elsevier, vol. 34(8), pages 1886-1898, August.
    19. repec:bla:jfinan:v:53:y:1998:i:3:p:1015-1052 is not listed on IDEAS
    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. Abakah, Emmanuel Joel Aikins & Caporale, Guglielmo Maria & Gil-Alana, Luis Alberiko, 2021. "Economic policy uncertainty: Persistence and cross-country linkages," Research in International Business and Finance, Elsevier, vol. 58(C).
    2. Mighri, Zouheir & Ragoubi, Hanen & Sarwar, Suleman & Wang, Yihan, 2022. "Quantile Granger causality between US stock market indices and precious metal prices," Resources Policy, Elsevier, vol. 76(C).
    3. Kanjilal, Kakali & Ghosh, Sajal, 2017. "Dynamics of crude oil and gold price post 2008 global financial crisis – New evidence from threshold vector error-correction model," Resources Policy, Elsevier, vol. 52(C), pages 358-365.
    4. Konstantinos N. Konstantakis & Panayotis G. Michaelides & Panos Xidonas & Stavroula Yfanti, 2025. "Carbon emissions and sustainability in Covid-19’s waves: evidence from a two-state dynamic Markov-switching regression (MSR) model," Annals of Operations Research, Springer, vol. 347(1), pages 217-239, April.
    5. Galán-Gutiérrez, Juan Antonio & Martín-García, Rodrigo, 2021. "Cointegration between the structure of copper futures prices and Brexit," Resources Policy, Elsevier, vol. 71(C).
    6. Vicente Esteve, 2004. "Política fiscal y productividad del trabajo en la economía española: un análisis de series temporales," Revista de Analisis Economico – Economic Analysis Review, Universidad Alberto Hurtado/School of Economics and Business, vol. 19(1), pages 3-29, June.
    7. 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).
    8. Apergis, Nicholas & Carmona-González, Nieves & Gil-Alana, Luis Alberiko, 2020. "Persistence in silver prices and the influence of solar energy," Resources Policy, Elsevier, vol. 69(C).
    9. Lei Pan & Svetlana Maslyuk-Escobedo & Vinod Mishra, 2019. "Carry Trade Returns and Commodity Prices under Capital and Interest Rate Controls: Empirical Evidence from China," Monash Economics Working Papers 16-18, Monash University, Department of Economics.
    10. Alqahtani, Abdullah & Klein, Tony, 2021. "Oil price changes, uncertainty, and geopolitical risks: On the resilience of GCC countries to global tensions," Energy, Elsevier, vol. 236(C).
    11. Yunus, Nafeesa, 2020. "Time-varying linkages among gold, stocks, bonds and real estate," The Quarterly Review of Economics and Finance, Elsevier, vol. 77(C), pages 165-185.
    12. Martin T. Bohl & Alexander Pütz & Pierre L. Siklos & Christoph Sulewski, 2018. "Information Transmission under Increasing Political Tension – Evidence for the Berlin Produce Exchange 1887-1896," CQE Working Papers 7618, Center for Quantitative Economics (CQE), University of Muenster.
    13. Takashi Miyazaki, 2019. "Clarifying the Response of Gold Return to Financial Indicators: An Empirical Comparative Analysis Using Ordinary Least Squares, Robust and Quantile Regressions," JRFM, MDPI, vol. 12(1), pages 1-18, February.
    14. Federico Carlini & Paolo Santucci de Magistris, 2019. "Resuscitating the co-fractional model of Granger (1986)," CREATES Research Papers 2019-02, Department of Economics and Business Economics, Aarhus University.
    15. Elgammal, Mohammed M. & Ahmed, Walid M.A. & Alshami, Abdullah, 2021. "Price and volatility spillovers between global equity, gold, and energy markets prior to and during the COVID-19 pandemic," Resources Policy, Elsevier, vol. 74(C).
    16. Harald Grech, 2004. "What Do German Short-Term Interest Rates Tell Us About Future Inflation?," Working Papers 94, Oesterreichische Nationalbank (Austrian Central Bank).
    17. Wang, Xinya & Lucey, Brian & Huang, Shupei, 2022. "Can gold hedge against oil price movements: Evidence from GARCH-EVT wavelet modeling," Journal of Commodity Markets, Elsevier, vol. 27(C).
    18. In Choi, 2012. "Panel Cointegration," Working Papers 1208, Nam Duck-Woo Economic Research Institute, Sogang University (Former Research Institute for Market Economy).
    19. Tarlok Singh, 2017. "Are Current Account Deficits in the OECD Countries Sustainable? Robust Evidence from Time-Series Estimators," The International Trade Journal, Taylor & Francis Journals, vol. 31(1), pages 29-64, January.
    20. Bent Jesper Christensen & Nabanita Datta Gupta & Paolo Santucci de Magistris, 2021. "Measuring the impact of clean energy production on CO2 abatement in Denmark: Upper bound estimation and forecasting," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(1), pages 118-149, January.

    More about this item

    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
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:ces:ceswps:_12559. 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: Klaus Wohlrabe (email available below). General contact details of provider: https://edirc.repec.org/data/cesifde.html .

    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.