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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

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  • 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
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    References listed on IDEAS

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    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. 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.
    3. 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.
    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.
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