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Forecasting Realized Gold Volatility: Is there a Role of Geopolitical Risks?

Author

Listed:
  • Konstantinos Gkillas

    () (Department of Business Administration, University of Patras-University Campus, Rio, P.O. Box 1391, 26500 Patras, Greece)

  • Rangan Gupta

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

  • Christian Pierdzioch

    () (Department of Economics, Helmut Schmidt University, Holstenhofweg 85, P.O.B. 700822, 22008 Hamburg, Germany)

Abstract

We use a quantile-regression heterogeneous autoregressive realized volatility (QR-HARRV) model to study whether geopolitical risks have predictive value in sample and out-of-sample for realized gold-returns volatility estimated from intradaily data. We consider overall geopolitical risks along with a decomposition into actual risks (i.e., acts) and threats, and we control for overall the impact of economic policy uncertainty (EPU). We find that, after controlling for EPU, the components of geopolitical risks have predictive power for realized volatility mainly at a longer forecast horizon when we account for the potential asymmetry of the loss function a forecaster uses to evaluate forecasts.

Suggested Citation

  • Konstantinos Gkillas & Rangan Gupta & Christian Pierdzioch, 2019. "Forecasting Realized Gold Volatility: Is there a Role of Geopolitical Risks?," Working Papers 201943, University of Pretoria, Department of Economics.
  • Handle: RePEc:pre:wpaper:201943
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    References listed on IDEAS

    as
    1. 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.
    2. Mehmet Balcilar & Rangan Gupta & Christian Pierdzioch, 2017. "On exchange-rate movements and gold-price fluctuations: evidence for gold-producing countries from a nonparametric causality-in-quantiles test," International Economics and Economic Policy, Springer, vol. 14(4), pages 691-700, October.
    3. Paolo Zagaglia & Massimiliano Marzo, 2013. "Gold and the U.S. dollar: tales from the turmoil," Quantitative Finance, Taylor & Francis Journals, vol. 13(4), pages 571-582, March.
    4. Bollerslev, Tim & Ghysels, Eric, 1996. "Periodic Autoregressive Conditional Heteroscedasticity," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(2), pages 139-151, April.
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    6. 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.
    7. Bouoiyour, Jamal & Selmi, Refk & Wohar, Mark E., 2018. "Measuring the response of gold prices to uncertainty: An analysis beyond the mean," Economic Modelling, Elsevier, vol. 75(C), pages 105-116.
    8. Haugom, Erik & Ray, Rina & Ullrich, Carl J. & Veka, Steinar & Westgaard, Sjur, 2016. "A parsimonious quantile regression model to forecast day-ahead value-at-risk," Finance Research Letters, Elsevier, vol. 16(C), pages 196-207.
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    13. Pierdzioch, Christian & Risse, Marian & Rohloff, Sebastian, 2015. "A real-time quantile-regression approach to forecasting gold returns under asymmetric loss," Resources Policy, Elsevier, vol. 45(C), pages 299-306.
    14. Baur, Dirk G., 2013. "The structure and degree of dependence: A quantile regression approach," Journal of Banking & Finance, Elsevier, vol. 37(3), pages 786-798.
    15. Pierdzioch, Christian & Risse, Marian & Rohloff, Sebastian, 2016. "A boosting approach to forecasting the volatility of gold-price fluctuations under flexible loss," Resources Policy, Elsevier, vol. 47(C), pages 95-107.
    16. 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.
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    Keywords

    Gold-price returns; Realized volatility; Geopolitical risks; Forecasting;

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