IDEAS home Printed from https://ideas.repec.org/a/wly/jforec/v44y2025i4p1478-1500.html
   My bibliography  Save this article

Forecasting Gold Volatility in an Uncertain Environment: The Roles of Large and Small Shock Sizes

Author

Listed:
  • Li Zhang
  • Lu Wang
  • Yu Ji
  • Zhigang Pan

Abstract

In a complex and volatile macroeconomic environment, precious metals, which have the functions of preservation, appreciation, and hedging, play an important role in investment risk management. Therefore, this study adopts the extended GARCH‐MIDAS model to investigate the underlying connection between gold price volatility and different uncertain shocks. In this paper, we consider five uncertainty indicators and then decompose them into different states to capture their shock sizes. Next, we introduce uncertainty shocks into the MIDAS structure to test whether they contain relevant and valid information about gold price volatility forecasts. Specifically, parameter significance suggests a positive association between uncertain indicators and gold price volatility, but variability in the influence of their shock sizes on gold price volatility. Out‐of‐sample results present that the extended model that includes asymmetric shock sizes outperforms other competitive models. Besides, the model that includes large shock sizes exhibits better predictive performance than the model that includes small shocks. Finally, based on the empirical analyses, this paper provides new insights for the gold industry, futures exchanges, government regulators, and investors engaged in futures hedging to achieve risk control and financial stability in response to uncertain shocks.

Suggested Citation

  • Li Zhang & Lu Wang & Yu Ji & Zhigang Pan, 2025. "Forecasting Gold Volatility in an Uncertain Environment: The Roles of Large and Small Shock Sizes," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 44(4), pages 1478-1500, July.
  • Handle: RePEc:wly:jforec:v:44:y:2025:i:4:p:1478-1500
    DOI: 10.1002/for.3247
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/for.3247
    Download Restriction: no

    File URL: https://libkey.io/10.1002/for.3247?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Reboredo, Juan C., 2013. "Is gold a safe haven or a hedge for the US dollar? Implications for risk management," Journal of Banking & Finance, Elsevier, vol. 37(8), pages 2665-2676.
    2. Bakas, Dimitrios & Triantafyllou, Athanasios, 2018. "The impact of uncertainty shocks on the volatility of commodity prices," Journal of International Money and Finance, Elsevier, vol. 87(C), pages 96-111.
    3. Aye, Goodness & Gupta, Rangan & Hammoudeh, Shawkat & Kim, Won Joong, 2015. "Forecasting the price of gold using dynamic model averaging," International Review of Financial Analysis, Elsevier, vol. 41(C), pages 257-266.
    4. Yimin Zhou & Liyan Han & Libo Yin, 2018. "Is the relationship between gold and the U.S. dollar always negative? The role of macroeconomic uncertainty," Applied Economics, Taylor & Francis Journals, vol. 50(4), pages 354-370, January.
    5. Triki, Mohamed Bilel & Ben Maatoug, Abderrazek, 2021. "The GOLD market as a safe haven against the stock market uncertainty: Evidence from geopolitical risk," Resources Policy, Elsevier, vol. 70(C).
    6. Bouoiyour, Jamal & Selmi, Refk & Wohar, Mark E., 2018. "Measuring the response of gold prices to uncertainty: An analysis beyond the mean," Economic Modelling, Elsevier, vol. 75(C), pages 105-116.
    7. Libing Fang & Baizhu Chen & Honghai Yu & Yichuo Qian, 2018. "The importance of global economic policy uncertainty in predicting gold futures market volatility: A GARCH‐MIDAS approach," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 38(3), pages 413-422, March.
    8. Pesaran, M Hashem & Timmermann, Allan, 1992. "A Simple Nonparametric Test of Predictive Performance," Journal of Business & Economic Statistics, American Statistical Association, vol. 10(4), pages 561-565, October.
    9. Balcilar, Mehmet & Gupta, Rangan & Pierdzioch, Christian, 2016. "Does uncertainty move the gold price? New evidence from a nonparametric causality-in-quantiles test," Resources Policy, Elsevier, vol. 49(C), pages 74-80.
    10. Mensi, Walid & Beljid, Makram & Boubaker, Adel & Managi, Shunsuke, 2013. "Correlations and volatility spillovers across commodity and stock markets: Linking energies, food, and gold," Economic Modelling, Elsevier, vol. 32(C), pages 15-22.
    11. Lu, Botao & Ma, Feng & Wang, Jiqian & Ding, Hui & Wahab, M.I.M., 2021. "Harnessing the decomposed realized measures for volatility forecasting: Evidence from the US stock market," International Review of Economics & Finance, Elsevier, vol. 72(C), pages 672-689.
    12. Michele Piffer & Maximilian Podstawski, 2018. "Identifying Uncertainty Shocks Using the Price of Gold," Economic Journal, Royal Economic Society, vol. 128(616), pages 3266-3284, December.
    13. Bakas, Dimitrios & Triantafyllou, Athanasios, 2020. "Commodity price volatility and the economic uncertainty of pandemics," Economics Letters, Elsevier, vol. 193(C).
    14. Selmi, Refk & Mensi, Walid & Hammoudeh, Shawkat & Bouoiyour, Jamal, 2018. "Is Bitcoin a hedge, a safe haven or a diversifier for oil price movements? A comparison with gold," Energy Economics, Elsevier, vol. 74(C), pages 787-801.
    15. Gonzalo, Jesus & Martinez, Oscar, 2006. "Large shocks vs. small shocks. (Or does size matter? May be so.)," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 311-347.
    16. Gkillas, Konstantinos & Gupta, Rangan & Pierdzioch, Christian, 2020. "Forecasting realized gold volatility: Is there a role of geopolitical risks?," Finance Research Letters, Elsevier, vol. 35(C).
    17. Clark, Todd E. & West, Kenneth D., 2007. "Approximately normal tests for equal predictive accuracy in nested models," Journal of Econometrics, Elsevier, vol. 138(1), pages 291-311, May.
    18. Barbara Rossi & Atsushi Inoue, 2012. "Out-of-Sample Forecast Tests Robust to the Choice of Window Size," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(3), pages 432-453, April.
    19. Joscha Beckmann & Theo Berger & Robert Czudaj, 2019. "Gold price dynamics and the role of uncertainty," Quantitative Finance, Taylor & Francis Journals, vol. 19(4), pages 663-681, April.
    20. Yaya, OlaOluwa S. & Tumala, Mohammed M. & Udomboso, Christopher G., 2016. "Volatility persistence and returns spillovers between oil and gold prices: Analysis before and after the global financial crisis," Resources Policy, Elsevier, vol. 49(C), pages 273-281.
    21. Chao Liang & Feng Ma & Lu Wang & Qing Zeng, 2021. "The information content of uncertainty indices for natural gas futures volatility forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(7), pages 1310-1324, November.
    22. Peter Reinhard Hansen & Asger Lunde & James M. Nason, 2003. "Choosing the Best Volatility Models: The Model Confidence Set Approach," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 65(s1), pages 839-861, December.
    23. Ruzhao Gao & Bing Zhang, 2016. "How does economic policy uncertainty drive gold--stock correlations? Evidence from the UK," Applied Economics, Taylor & Francis Journals, vol. 48(33), pages 3081-3087, July.
    24. El Hedi Arouri, Mohamed & Lahiani, Amine & Nguyen, Duc Khuong, 2015. "World gold prices and stock returns in China: Insights for hedging and diversification strategies," Economic Modelling, Elsevier, vol. 44(C), pages 273-282.
    25. Ruzhao Gao & Yancai Zhao & Bing Zhang, 2021. "The spillover effects of economic policy uncertainty on the oil, gold, and stock markets: Evidence from China," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(2), pages 2134-2141, April.
    26. Yu, Fanchao, 2023. "Macroeconomic information, global economic policy uncertainty and gold futures return predictability," Finance Research Letters, Elsevier, vol. 55(PA).
    27. Xiafei Li & Dongxin Li & Xuhui Zhang & Guiwu Wei & Lan Bai & Yu Wei, 2021. "Forecasting regular and extreme gold price volatility: The roles of asymmetry, extreme event, and jump," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(8), pages 1501-1523, December.
    28. Bams, Dennis & Blanchard, Gildas & Honarvar, Iman & Lehnert, Thorsten, 2017. "Does oil and gold price uncertainty matter for the stock market?," Journal of Empirical Finance, Elsevier, vol. 44(C), pages 270-285.
    29. Yu, Honghai & Fang, Libing & Sun, Wencong, 2018. "Forecasting performance of global economic policy uncertainty for volatility of Chinese stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 505(C), pages 931-940.
    30. Yilanci, Veli & Kilci, Esra N., 2021. "The role of economic policy uncertainty and geopolitical risk in predicting prices of precious metals: Evidence from a time-varying bootstrap causality test," Resources Policy, Elsevier, vol. 72(C).
    31. Feng Ma & Xinjie Lu & Lu Wang & Julien Chevallier, 2021. "Global economic policy uncertainty and gold futures market volatility: Evidence from Markov regime‐switching GARCH‐MIDAS models," Post-Print halshs-04250272, HAL.
    32. Wei, Yu & Wang, Yizhi & Lucey, Brian M. & Vigne, Samuel A., 2023. "Cryptocurrency uncertainty and volatility forecasting of precious metal futures markets," Journal of Commodity Markets, Elsevier, vol. 29(C).
    33. Chen, Rongda & Xu, Jianjun, 2019. "Forecasting volatility and correlation between oil and gold prices using a novel multivariate GAS model," Energy Economics, Elsevier, vol. 78(C), pages 379-391.
    34. Li, Xiafei & Liang, Chao & Chen, Zhonglu & Umar, Muhammad, 2022. "Forecasting crude oil volatility with uncertainty indicators: New evidence," Energy Economics, Elsevier, vol. 108(C).
    35. Chen, Peng & Miao, Xinru & Tee, Kai-Hong, 2023. "Do gold prices respond more to uncertainty shocks at the zero lower bound?," Resources Policy, Elsevier, vol. 86(PA).
    36. Chao Liang & Yu Wei & Yaojie Zhang, 2020. "Is implied volatility more informative for forecasting realized volatility: An international perspective," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(8), pages 1253-1276, December.
    37. 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).
    38. Wang, Lu & Ma, Feng & Liu, Jing & Yang, Lin, 2020. "Forecasting stock price volatility: New evidence from the GARCH-MIDAS model," International Journal of Forecasting, Elsevier, vol. 36(2), pages 684-694.
    39. Thomas C. Chiang, 2022. "Can gold or silver be used as a hedge against policy uncertainty and COVID-19 in the Chinese market?," China Finance Review International, Emerald Group Publishing Limited, vol. 12(4), pages 571-600, June.
    40. Feng Ma & Xinjie Lu & Lu Wang & Julien Chevallier, 2021. "Global economic policy uncertainty and gold futures market volatility: Evidence from Markov regime‐switching GARCH‐MIDAS models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(6), pages 1070-1085, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Li, Xiafei & Guo, Qiang & Liang, Chao & Umar, Muhammad, 2023. "Forecasting gold volatility with geopolitical risk indices," Research in International Business and Finance, Elsevier, vol. 64(C).
    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. Rangan Gupta & Sayar Karmakar & Christian Pierdzioch, 2024. "Safe Havens, Machine Learning, and the Sources of Geopolitical Risk: A Forecasting Analysis Using Over a Century of Data," Computational Economics, Springer;Society for Computational Economics, vol. 64(1), pages 487-513, July.
    4. 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).
    5. Asai, Manabu & Gupta, Rangan & McAleer, Michael, 2020. "Forecasting volatility and co-volatility of crude oil and gold futures: Effects of leverage, jumps, spillovers, and geopolitical risks," International Journal of Forecasting, Elsevier, vol. 36(3), pages 933-948.
    6. Feng Ma & Xinjie Lu & Lu Wang & Julien Chevallier, 2021. "Global economic policy uncertainty and gold futures market volatility: Evidence from Markov regime‐switching GARCH‐MIDAS models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(6), pages 1070-1085, September.
    7. Golitsis, Petros & Gkasis, Pavlos & Bellos, Sotirios K., 2022. "Dynamic spillovers and linkages between gold, crude oil, S&P 500, and other economic and financial variables. Evidence from the USA," The North American Journal of Economics and Finance, Elsevier, vol. 63(C).
    8. Wang, Lu & Wu, Jiangbin & Cao, Yang & Hong, Yanran, 2022. "Forecasting renewable energy stock volatility using short and long-term Markov switching GARCH-MIDAS models: Either, neither or both?," Energy Economics, Elsevier, vol. 111(C).
    9. Guo, Xiaozhu & Huang, Dengshi & Li, Xiafei & Liang, Chao, 2023. "Are categorical EPU indices predictable for carbon futures volatility? Evidence from the machine learning method," International Review of Economics & Finance, Elsevier, vol. 83(C), pages 672-693.
    10. Bouoiyour, Jamal & Selmi, Refk & Wohar, Mark E., 2018. "Measuring the response of gold prices to uncertainty: An analysis beyond the mean," Economic Modelling, Elsevier, vol. 75(C), pages 105-116.
    11. Bonato, Matteo & Gupta, Rangan & Lau, Chi Keung Marco & Wang, Shixuan, 2020. "Moments-based spillovers across gold and oil markets," Energy Economics, Elsevier, vol. 89(C).
    12. Guo, Xiaozhu & Huang, Yisu & Liang, Chao & Umar, Muhammad, 2022. "Forecasting volatility of EUA futures: New evidence," Energy Economics, Elsevier, vol. 110(C).
    13. Díaz, Juan D. & Hansen, Erwin & Cabrera, Gabriel, 2023. "Gold risk premium estimation with machine learning methods," Journal of Commodity Markets, Elsevier, vol. 31(C).
    14. Chi-Wei Su & Lidong Pang & Muhammad Umar & Oana-Ramona Lobonţ, 2022. "Will Gold Always Shine amid World Uncertainty?," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 58(12), pages 3425-3438, September.
    15. Qin, Meng & Su, Chi-Wei & Tao, Ran & Umar, Muhammad, 2020. "Is factionalism a push for gold price?," Resources Policy, Elsevier, vol. 67(C).
    16. Chen, Juan & Xiao, Zuoping & Bai, Jiancheng & Guo, Hongling, 2023. "Predicting volatility in natural gas under a cloud of uncertainties," Resources Policy, Elsevier, vol. 82(C).
    17. Su, Chi-Wei & Pang, Lidong & Umar, Muhammad & Lobonţ, Oana-Ramona & Moldovan, Nicoleta-Claudia, 2022. "Does gold's hedging uncertainty aura fade away?," Resources Policy, Elsevier, vol. 77(C).
    18. Salisu, Afees A. & Vo, Xuan Vinh & Lawal, Adedoyin, 2021. "Hedging oil price risk with gold during COVID-19 pandemic," Resources Policy, Elsevier, vol. 70(C).
    19. Chiang, Thomas C., 2022. "The effects of economic uncertainty, geopolitical risk and pandemic upheaval on gold prices," Resources Policy, Elsevier, vol. 76(C).
    20. Wang, Lu & Ma, Feng & Hao, Jianyang & Gao, Xinxin, 2021. "Forecasting crude oil volatility with geopolitical risk: Do time-varying switching probabilities play a role?," International Review of Financial Analysis, Elsevier, vol. 76(C).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wly:jforec:v:44:y:2025:i:4:p:1478-1500. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: http://www3.interscience.wiley.com/cgi-bin/jhome/2966 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.