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Predicting gold volatility: Exploring the impact of extreme risk in the international commodity market

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  • Tang, Yusui
  • Zhong, Juandan

Abstract

This paper mainly examines the performance of the tail risk (TR) constructed by 19 sub-categories of commodity price indices for predicting gold futures market volatility. Under the framework of the AR model, we compared the predictive capabilities of AR-type models incorporating the TR indicator as an additional explanatory variable against the AR benchmark model without the TR indicator. Our findings reveal that the incorporation of the TR indicator significantly improves the predictive accuracy of the gold futures volatility model. Notably, this enhancement is particularly pronounced during periods of heightened volatility.

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  • Tang, Yusui & Zhong, Juandan, 2023. "Predicting gold volatility: Exploring the impact of extreme risk in the international commodity market," Finance Research Letters, Elsevier, vol. 58(PB).
    • Handle: RePEc:eee:finlet:v:58:y:2023:i:pb:s1544612323008632
    DOI: 10.1016/j.frl.2023.104491
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    1. Huang, Darien & Kilic, Mete, 2019. "Gold, platinum, and expected stock returns," Journal of Financial Economics, Elsevier, vol. 132(3), pages 50-75.
    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. Smales, Lee A., 2014. "News sentiment in the gold futures market," Journal of Banking & Finance, Elsevier, vol. 49(C), pages 275-286.
    4. Bryan Kelly & Hao Jiang, 2014. "Editor's Choice Tail Risk and Asset Prices," The Review of Financial Studies, Society for Financial Studies, vol. 27(10), pages 2841-2871.
    5. Enilov, Martin & Mensi, Walid & Stankov, Petar, 2023. "Does safe haven exist? Tail risks of commodity markets during COVID-19 pandemic," Journal of Commodity Markets, Elsevier, vol. 29(C).
    6. Ma, Feng & Guo, Yangli & Chevallier, Julien & Huang, Dengshi, 2022. "Macroeconomic attention, economic policy uncertainty, and stock volatility predictability," International Review of Financial Analysis, Elsevier, vol. 84(C).
    7. Junttila, Juha & Pesonen, Juho & Raatikainen, Juhani, 2018. "Commodity market based hedging against stock market risk in times of financial crisis: The case of crude oil and gold," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 56(C), pages 255-280.
    8. Zhu, Xuehong & Niu, Zibo & Zhang, Hongwei & Huang, Jiaxin & Zuo, Xuguang, 2022. "Can gold and bitcoin hedge against the COVID-19 related news sentiment risk? New evidence from a NARDL approach," Resources Policy, Elsevier, vol. 79(C).
    9. John Y. Campbell & Samuel B. Thompson, 2008. "Predicting Excess Stock Returns Out of Sample: Can Anything Beat the Historical Average?," The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1509-1531, July.
    10. Salisu, Afees A. & Adediran, Idris & Omoke, Philip C. & Tchankam, Jean Paul, 2023. "Gold and tail risks," Resources Policy, Elsevier, vol. 80(C).
    11. Gu, Chen & Kurov, Alexander & Stan, Raluca, 2023. "Monetary policy and uncertainty resolution in commodity markets," Finance Research Letters, Elsevier, vol. 55(PA).
    12. Zhang, Yongmin & Ding, Shusheng & Scheffel, Eric M., 2019. "A key determinant of commodity price Co-movement: The role of daily market liquidity," Economic Modelling, Elsevier, vol. 81(C), pages 170-180.
    13. Zhang, Zhengyong & Shahzad, Syed Jawad Hussain & Bouri, Elie, 2022. "Tail risk transmission from commodity prices to sovereign risk of emerging economies," Resources Policy, Elsevier, vol. 78(C).
    14. Mahdavi, Saeid & Zhou, Su, 1997. "Gold and commodity prices as leading indicators of inflation: Tests of long-run relationship and predictive performance," Journal of Economics and Business, Elsevier, vol. 49(5), pages 475-489.
    15. Hu, Yang & Lang, Chunlin & Corbet, Shaen & Hou, Yang (Greg) & Oxley, Les, 2023. "Exploring the dynamic behaviour of commodity market tail risk connectedness during the negative WTI pricing event," Energy Economics, Elsevier, vol. 125(C).
    16. Feng Ma & Yangli Guo & Julien Chevallier & Dengshi Huang, 2022. "Macroeconomic attention, economic policy uncertainty, and stock volatility predictability," Post-Print halshs-04250304, HAL.
    17. Manuel Ammann & Mathis Moerke & Marcel Prokopczuk & Christoph Matthias Würsig, 2023. "Commodity tail risks," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 43(2), pages 168-197, February.
    18. Ma, Feng & Li, Yu & Liu, Li & Zhang, Yaojie, 2018. "Are low-frequency data really uninformative? A forecasting combination perspective," The North American Journal of Economics and Finance, Elsevier, vol. 44(C), pages 92-108.
    19. Lübbers, Johannes & Posch, Peter N., 2016. "Commodities' common factor: An empirical assessment of the markets' drivers," Journal of Commodity Markets, Elsevier, vol. 4(1), pages 28-40.
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