Forecasting Power of Infectious Diseases-Related Uncertainty for Gold Realized Volatility
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Cited by:
- 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).
- Afees A. Salisu & Christian Pierdzioch & Rangan Gupta, 2021. "Oil Tail Risks and the Forecastability of the Realized Variance of Oil-Price: Evidence from Over 150 Years of Data," Working Papers 202146, University of Pretoria, Department of Economics.
- 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).
- Jiawen Luo & Riza Demirer & Rangan Gupta & Qiang Ji, 2021. "Forecasting Oil and Gold Volatilities with Sentiment Indicators Under Structural Breaks," Working Papers 202130, University of Pretoria, Department of Economics.
- 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.
- 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).
- Oguzhan Cepni & Rangan Gupta & Daniel Pienaar & Christian Pierdzioch, 2022. "Forecasting the Realized Variance of Oil-Price Returns Using Machine-Learning: Is there a Role for U.S. State-Level Uncertainty?," Working Papers 202205, University of Pretoria, Department of Economics.
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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