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Forecasting power of infectious diseases-related uncertainty for gold realized variance

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  • Bouri, Elie
  • Gkillas, Konstantinos
  • Gupta, Rangan
  • Pierdzioch, Christian

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 variance (HAR-RV) model. Our results show that the EMVID index increases realized variance (RV) at the highest level of statistical significance within-sample, while it improves the forecast accuracy of gold realized variance at short-, medium-, and long-run horizons in a statistically significant manner. Importantly, by assessing the role of this index during the recent pandemic, we find strong evidence for its critical role in forecasting gold RV. Such evidence has important portfolio implications for investors during the current period of unprecedented levels of uncertainty resulting from the outbreak of COVID-19.

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  • Bouri, Elie & Gkillas, Konstantinos & Gupta, Rangan & Pierdzioch, Christian, 2021. "Forecasting power of infectious diseases-related uncertainty for gold realized variance," Finance Research Letters, Elsevier, vol. 42(C).
    • Handle: RePEc:eee:finlet:v:42:y:2021:i:c:s1544612321000179
    DOI: 10.1016/j.frl.2021.101936
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    References listed on IDEAS

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    Cited by:

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    6. Castillo, Brenda & León, Ángel & Ñíguez, Trino-Manuel, 2021. "Backtesting VaR under the COVID-19 sudden changes in volatility," Finance Research Letters, Elsevier, vol. 43(C).
    7. Thanoj K. Muddana & Komal S.R. Bhimireddy & Anandamayee Majumdar & Rangan Gupta, 2024. "Forecasting Gold Returns Volatility Over 1258-2023: The Role of Moments," Working Papers 202421, University of Pretoria, Department of Economics.
    8. Gök, Remzi & Bouri, Elie & Gemici, Eray, 2022. "Can Twitter-based economic uncertainty predict safe-haven assets under all market conditions and investment horizons?," Technological Forecasting and Social Change, Elsevier, vol. 185(C).
    9. Al Rababa'a, Abdel Razzaq & Alomari, Mohammad & Mensi, Walid & Matar, Ali & Saidat, Zaid, 2021. "Does tracking the infectious diseases impact the gold, oil and US dollar returns and correlation? A quantile regression approach," Resources Policy, Elsevier, vol. 74(C).
    10. 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.
    11. Shang, Yue & Wei, Yu & Chen, Yongfei, 2022. "Cryptocurrency policy uncertainty and gold return forecasting: A dynamic Occam's window approach," Finance Research Letters, Elsevier, vol. 50(C).
    12. Iyer, Subramanian Rama & Simkins, Betty J., 2022. "COVID-19 and the Economy: Summary of research and future directions," Finance Research Letters, Elsevier, vol. 47(PB).
    13. Dash, Saumya Ranjan & Maitra, Debasish, 2022. "The COVID-19 pandemic uncertainty, investor sentiment, and global equity markets: Evidence from the time-frequency co-movements," The North American Journal of Economics and Finance, Elsevier, vol. 62(C).
    14. Imran Yousaf & Vasilios Plakandaras & Elie Bouri & Rangan Gupta, 2022. "Hedge and Safe Haven Properties of Gold, US Treasury, Bitcoin, and Dollar/CHF against the FAANA Companies and S&P 500," Working Papers 202227, University of Pretoria, Department of Economics.
    15. Yousaf, Imran & Plakandaras, Vasilios & Bouri, Elie & Gupta, Rangan, 2023. "Hedge and safe-haven properties of FAANA against gold, US Treasury, bitcoin, and US Dollar/CHF during the pandemic period," The North American Journal of Economics and Finance, Elsevier, vol. 64(C).
    16. Esam Mahdi & Ameena Al-Abdulla, 2022. "Impact of COVID-19 Pandemic News on the Cryptocurrency Market and Gold Returns: A Quantile-on-Quantile Regression Analysis," Econometrics, MDPI, vol. 10(2), pages 1-14, June.
    17. Tamilselvan, M. & Halder, Abhishek & Kannadhasan, M., 2024. "Exploring the ingredients, mixtures, and inclinations of geopolitical risk," International Review of Economics & Finance, Elsevier, vol. 90(C), pages 187-206.
    18. Mo, Bin & Nie, He & Zhao, Rongjie, 2024. "Dynamic nonlinear effects of geopolitical risks on commodities: Fresh evidence from quantile methods," Energy, Elsevier, vol. 288(C).
    19. Ştefan Cristian Gherghina & Liliana Nicoleta Simionescu, 2023. "Exploring the asymmetric effect of COVID-19 pandemic news on the cryptocurrency market: evidence from nonlinear autoregressive distributed lag approach and frequency domain causality," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-58, December.
    20. Bouri, Elie & Gupta, Rangan & Nel, Jacobus & Shiba, Sisa, 2022. "Contagious diseases and gold: Over 700 years of evidence from quantile regressions," Finance Research Letters, Elsevier, vol. 50(C).
    21. Konstantinos Gkillas & Christoforos Konstantatos & Costas Siriopoulos, 2021. "Uncertainty Due to Infectious Diseases and Stock–Bond Correlation," Econometrics, MDPI, vol. 9(2), pages 1-18, April.
    22. Dutta, Anupam & Park, Donghyun & Uddin, Gazi Salah & Kanjilal, Kakali & Ghosh, Sajal, 2024. "Do dirty and clean energy investments react to infectious disease-induced uncertainty?," Technological Forecasting and Social Change, Elsevier, vol. 205(C).

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    More about this item

    Keywords

    Uncertainty; Infectious diseases; COVID-19; Gold; Realized variance; 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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