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Forecasting Power of Infectious Diseases-Related Uncertainty for Gold Realized Volatility

Author

Listed:
  • Elie Bouri

    (USEK Business School, Holy Spirit University of Kaslik, Jounieh, Lebanon)

  • 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, 0002, South Africa)

  • Christian Pierdzioch

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

Abstract

We examine the forecasting power of a daily newspaper-based index of uncertainty associated with infectious diseases (EMVID) for gold market returns volatility via the heterogeneous autoregressive realized volatility (HAR-RV) model. Our results show that the EMVID index increases RV at the highest level of statistical significance within-sample, and more importantly, it also improves forecast accuracy of gold realized volatility at short-, medium, and long-run horizons in a statistically significant manner. Our results have important portfolio implications for investors during the current period of unprecedented levels of uncertainty resulting from the outbreak of COVID-19.

Suggested Citation

  • Elie Bouri & Konstantinos Gkillas & Rangan Gupta & Christian Pierdzioch, 2020. "Forecasting Power of Infectious Diseases-Related Uncertainty for Gold Realized Volatility," Working Papers 202049, University of Pretoria, Department of Economics.
    • Handle: RePEc:pre:wpaper:202049
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    Citations

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    Cited by:

    1. Salisu, Afees A. & Pierdzioch, Christian & Gupta, Rangan, 2022. "Oil tail risks and the forecastability of the realized variance of oil-price: Evidence from over 150 years of data," Finance Research Letters, Elsevier, vol. 46(PB).
    2. Luo, Jiawen & Demirer, Riza & Gupta, Rangan & Ji, Qiang, 2022. "Forecasting oil and gold volatilities with sentiment indicators under structural breaks," Energy Economics, Elsevier, vol. 105(C).
    3. Çepni, Oğuzhan & Gupta, Rangan & Pienaar, Daniel & Pierdzioch, Christian, 2022. "Forecasting the realized variance of oil-price returns using machine learning: Is there a role for U.S. state-level uncertainty?," Energy Economics, Elsevier, vol. 114(C).
    4. Celso-Arellano, Pedro & Gualajara, Victor & Coronado, Semei & Martinez, Jose N. & Venegas-Martínez, Francisco, 2023. "Impact of the global fear index (covid-19 panic) on the S&P global indices associated with natural resources, agribusiness, energy, metals and mining: Granger Causality and Shannon and Rényi Transfer ," MPRA Paper 117138, University Library of Munich, Germany, revised 06 Feb 2023.

    More about this item

    Keywords

    Uncertainty; Infectious Diseases; COVID-19; Gold; Realized Volatility; Forecasting;
    All these 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
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • D80 - Microeconomics - - Information, Knowledge, and Uncertainty - - - General
    • Q02 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General - - - Commodity Market

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