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A Note on Forecasting the Historical Realized Variance of Oil-Price Movements: The Role of Gold-to-Silver and Gold-to-Platinum Price Ratios

Author

Listed:
  • Rangan Gupta

    (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa)

  • Christian Pierdzioch

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

  • Wing-Keung Wong

    (Department of Finance, Fintech & Blockchain Research Center, and Big Data Research Center Asia University; Department of Medical Research China Medical University Hospital; Department of Economics and Finance, The Hang Seng University of Hong Kong)

Abstract

We examine the predictive value of gold-to-silver and gold-to-platinum price ratios, as proxies for global risks, for the realized variance (RV) of oil-price movements using the longest possible monthly data available over 1915:01-2021:03 and 1968:01-2021:03, respectively. Using the two ratios, we find statistically significant evidence for both ratios of in-sample predictability emanating for increases in RV. This finding also translates into statistically significant out-of-sample forecasting gains derived from these two ratios for RV. Given the importance of real-time forecasts of the volatility of oil-price movements, our results have important implications for investors and policymakers.

Suggested Citation

  • Rangan Gupta & Christian Pierdzioch & Wing-Keung Wong, 2021. "A Note on Forecasting the Historical Realized Variance of Oil-Price Movements: The Role of Gold-to-Silver and Gold-to-Platinum Price Ratios," Working Papers 202158, University of Pretoria, Department of Economics.
    • Handle: RePEc:pre:wpaper:202158
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    Cited by:

    1. Renchu Guan & Aoqing Wang & Yanchun Liang & Jiasheng Fu & Xiaosong Han, 2022. "International Natural Gas Price Trends Prediction with Historical Prices and Related News," Energies, MDPI, vol. 15(10), pages 1-14, May.
    2. Cui, Moyang & Wong, Wing-Keung & Wisetsri, Worakamol & Mabrouk, Fatma & Muda, Iskandar & Li, Zeyun & Hassan, Marria, 2023. "Do oil, gold and metallic price volatilities prove gold as a safe haven during COVID-19 pandemic? Novel evidence from COVID-19 data," Resources Policy, Elsevier, vol. 80(C).
    3. Peipei, Wang & James, William, 2024. "Predicting oil price fluctuations: Integrating external indicators and advanced regression techniques," Resources Policy, Elsevier, vol. 97(C).
    4. Salisu, Afees A. & Gupta, Rangan & Karmakar, Sayar & Das, Sonali, 2022. "Forecasting output growth of advanced economies over eight centuries: The role of gold market volatility as a proxy of global uncertainty," Resources Policy, Elsevier, vol. 75(C).
    5. Salisu, Afees A. & Pierdzioch, Christian & Gupta, Rangan & Gabauer, David, 2022. "Forecasting stock-market tail risk and connectedness in advanced economies over a century: The role of gold-to-silver and gold-to-platinum price ratios," International Review of Financial Analysis, Elsevier, vol. 83(C).
    6. Mehmet Balcilar & David Gabauer & Rangan Gupta & Christian Pierdzioch, 2023. "Climate Risks and Forecasting Stock Market Returns in Advanced Economies over a Century," Mathematics, MDPI, vol. 11(9), pages 1-21, April.
    7. Cheng, Hui-Ching & Shu, Ming-Hung & Huang, Jui-Chan, 2024. "Economic strategies for efficient use of natural resources: The impact of carbon taxation and fiscal policy," Resources Policy, Elsevier, vol. 92(C).
    8. Dan Sun, 2024. "RETRACTED ARTICLE: The role of green technology and green utility performance in advancing energy utilities' commitment to sustainability," Economic Change and Restructuring, Springer, vol. 57(2), pages 1-24, April.
    9. Zhang, Xiheng & Liu, Jiayu & Zhang, Kaiqi & Robert, James, 2023. "Analysis of firm performance in presence of oil price shocks: Importance of skilled management," Resources Policy, Elsevier, vol. 86(PA).

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    Keywords

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    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
    • Q02 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General - - - Commodity Market

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