IDEAS home Printed from https://ideas.repec.org/a/gam/jecnmx/v12y2024i4p38-d1541987.html
   My bibliography  Save this article

Financial Uncertainty and Gold Market Volatility: Evidence from a Generalized Autoregressive Conditional Heteroskedasticity Variant of the Mixed-Data Sampling (GARCH-MIDAS) Approach with Variable Selection

Author

Listed:
  • O-Chia Chuang

    (School of Digital Economics, Hubei University of Economics, Wuhan 430205, China)

  • Rangan Gupta

    (Department of Economics, University of Pretoria, Hatfield 0028, South Africa)

  • Christian Pierdzioch

    (Department of Economics, Helmut-Schmidt-University, 22008 Hamburg, Germany)

  • Buliao Shu

    (Economics and Management School, Wuhan University, Wuhan 430072, China)

Abstract

We analyze the predictive effect of monthly global, regional, and country-level financial uncertainties on daily gold market volatility using univariate and multivariate GARCH-MIDAS models, with the latter characterized by variable selection. Based on data over the period of July 1992 to May 2020, we highlight the role of the global financial uncertainty factor in accurately forecasting gold price volatility relative to the benchmark GARCH-MIDAS-realized volatility model, with a dominant role of European financial uncertainties, and 36 out of the 42 regional financial market uncertainties. The forecasting performance of the global financial uncertainty factor is as good as an index of global economic conditions, with results based on a combination of these two models depicting evidence of complementary information. Moreover, the GARCH-MIDAS model with global financial uncertainty cannot be outperformed by the multivariate version of the GARCH-MIDAS framework, estimated using the adaptive LASSO, involving the top five developed and developing countries each, chosen based on their ability to explain the movements of overall global financial uncertainty. Our results imply that as financial uncertainties can improve the accuracy of the forecasts of gold returns volatility, it would help investors to design optimal portfolios to counteract financial risks. Also, as gold returns volatility reflects financial uncertainty, accurate forecasts of it would provide information about the future path of economic activity, and assist policy authorities in preventing possible economic slowdowns.

Suggested Citation

  • O-Chia Chuang & Rangan Gupta & Christian Pierdzioch & Buliao Shu, 2024. "Financial Uncertainty and Gold Market Volatility: Evidence from a Generalized Autoregressive Conditional Heteroskedasticity Variant of the Mixed-Data Sampling (GARCH-MIDAS) Approach with Variable Sele," Econometrics, MDPI, vol. 12(4), pages 1-17, December.
    • Handle: RePEc:gam:jecnmx:v:12:y:2024:i:4:p:38-:d:1541987
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2225-1146/12/4/38/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2225-1146/12/4/38/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Reboredo, Juan C., 2013. "Is gold a safe haven or a hedge for the US dollar? Implications for risk management," Journal of Banking & Finance, Elsevier, vol. 37(8), pages 2665-2676.
    2. Park, Cyn-Young & Mercado, Rogelio V., 2014. "Determinants of financial stress in emerging market economies," Journal of Banking & Finance, Elsevier, vol. 45(C), pages 199-224.
    3. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    4. Agyei-Ampomah, Sam & Gounopoulos, Dimitrios & Mazouz, Khelifa, 2014. "Does gold offer a better protection against losses in sovereign debt bonds than other metals?," Journal of Banking & Finance, Elsevier, vol. 40(C), pages 507-521.
    5. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
    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. Salisu, Afees A. & Raheem, Ibrahim D. & Vo, Xuan Vinh, 2021. "Assessing the safe haven property of the gold market during COVID-19 pandemic," International Review of Financial Analysis, Elsevier, vol. 74(C).
    2. Rangan Gupta & Sayar Karmakar & Christian Pierdzioch, 2024. "Safe Havens, Machine Learning, and the Sources of Geopolitical Risk: A Forecasting Analysis Using Over a Century of Data," Computational Economics, Springer;Society for Computational Economics, vol. 64(1), pages 487-513, July.
    3. Gkillas, Konstantinos & Gupta, Rangan & Pierdzioch, Christian, 2020. "Forecasting realized gold volatility: Is there a role of geopolitical risks?," Finance Research Letters, Elsevier, vol. 35(C).
    4. Rangan Gupta & Qiang Ji & Christian Pierdzioch, 2024. "Climate Policy Uncertainty and Financial Stress: Evidence for China," Working Papers 202428, University of Pretoria, Department of Economics.
    5. Aktham Maghyereh & Hussein Abdoh, 2022. "Global financial crisis versus COVID‐19: Evidence from sentiment analysis," International Finance, Wiley Blackwell, vol. 25(2), pages 218-248, August.
    6. Asai, Manabu & Gupta, Rangan & McAleer, Michael, 2020. "Forecasting volatility and co-volatility of crude oil and gold futures: Effects of leverage, jumps, spillovers, and geopolitical risks," International Journal of Forecasting, Elsevier, vol. 36(3), pages 933-948.
    7. O-Chia Chuang & Rangan Gupta & Christian Pierdzioch & Buliao Shu, 2024. "Financial Uncertainty and Gold Market Volatility: Evidence from a GARCH-MIDAS Approach with Variable Selection," Working Papers 202441, University of Pretoria, Department of Economics.
    8. Troster, Victor & Bouri, Elie & Roubaud, David, 2019. "A quantile regression analysis of flights-to-safety with implied volatilities," Resources Policy, Elsevier, vol. 62(C), pages 482-495.
    9. Kelly Burns & Imad Moosa, 2017. "Demystifying the Meese–Rogoff puzzle: structural breaks or measures of forecasting accuracy?," Applied Economics, Taylor & Francis Journals, vol. 49(48), pages 4897-4910, October.
    10. Carlo Altavilla & Paul De Grauwe, 2010. "Forecasting and combining competing models of exchange rate determination," Applied Economics, Taylor & Francis Journals, vol. 42(27), pages 3455-3480.
    11. Gkillas, Konstantinos & Gupta, Rangan & Pierdzioch, Christian, 2020. "Forecasting realized oil-price volatility: The role of financial stress and asymmetric loss," Journal of International Money and Finance, Elsevier, vol. 104(C).
    12. Xiaojie Xu, 2017. "The rolling causal structure between the Chinese stock index and futures," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 31(4), pages 491-509, November.
    13. Dal Bianco, Marcos & Camacho, Maximo & Perez Quiros, Gabriel, 2012. "Short-run forecasting of the euro-dollar exchange rate with economic fundamentals," Journal of International Money and Finance, Elsevier, vol. 31(2), pages 377-396.
    14. Daniel Buncic, 2012. "Understanding forecast failure of ESTAR models of real exchange rates," Empirical Economics, Springer, vol. 43(1), pages 399-426, August.
    15. Manabu Asai & Rangan Gupta & Michael McAleer, 2019. "The Impact of Jumps and Leverage in Forecasting the Co-Volatility of Oil and Gold Futures," Energies, MDPI, vol. 12(17), pages 1-17, September.
    16. Akhtaruzzaman, Md & Boubaker, Sabri & Lucey, Brian M. & Sensoy, Ahmet, 2021. "Is gold a hedge or a safe-haven asset in the COVID–19 crisis?," Economic Modelling, Elsevier, vol. 102(C).
    17. Scott Brave & R. Andrew Butters & Alejandro Justiniano, 2016. "Forecasting Economic Activity with Mixed Frequency Bayesian VARs," Working Paper Series WP-2016-5, Federal Reserve Bank of Chicago.
    18. Máximo Camacho & Rafael Doménech, 2012. "MICA-BBVA: a factor model of economic and financial indicators for short-term GDP forecasting," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 3(4), pages 475-497, December.
    19. Keen Meng Choy & Hwee Kwan Chow, 2004. "Forecasting the Global Electronics Cycle with Leading Indicators: A VAR Approach," Econometric Society 2004 Australasian Meetings 223, Econometric Society.
    20. Ard H.J. den Reijer, 2005. "Forecasting Dutch GDP using Large Scale Factor Models," DNB Working Papers 028, Netherlands Central Bank, Research Department.

    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:gam:jecnmx:v:12:y:2024:i:4:p:38-:d:1541987. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.