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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

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  • Rangan Gupta

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

  • Christian Pierdzioch

    (Department of Economics, Helmut Schmidt University, 22008 Hamburg, Germany)

  • Wing-Keung Wong

    (Department of Finance, Fintech & Blockchain Research Center, and Big Data Research Center, Asia University, Taichung City 41354, Taiwan
    Department of Medical Research, China Medical University Hospital, Taichung City 40447, Taiwan
    Department of Economics and Finance, The Hang Seng University of Hong Kong, Siu Lek Yuen 41354, Hong Kong)

Abstract

We examine the predictive value of gold-to-silver and gold-to-platinum price ratios, as proxies for global risks affecting the realized variance ( RV ) of oil-price movements, using monthly data over the longest available periods of 1915:01–2021:03 and 1968:01–2021:03, respectively. Using the two ratios, we find statistically significant evidence of in-sample predictability for increases in RV for both ratios. 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," Energies, MDPI, vol. 14(20), pages 1-12, October.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:20:p:6775-:d:658457
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    Cited by:

    1. 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.
    2. 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.
    3. 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).
    4. 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).
    5. 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).
    6. 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.
    7. 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).
    8. 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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    More about this item

    Keywords

    gold-to-silver and gold-to-platinum price ratios; realized variance of oil price; 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
    • Q02 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General - - - Commodity Market

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