IDEAS home Printed from https://ideas.repec.org/a/ers/ijebaa/vviiiy2020i4p353-364.html
   My bibliography  Save this article

The Impact of COVID-19 on Gold Price Volatility

Author

Listed:
  • Ibrahim Yousef
  • Esam Shehadeh

Abstract

Purpose: This article investigates the implications of the spread of COVID-19 on gold spot prices. Design/Methodology/Approach: We use GARCH and GJR-GARCH models based on daily gold returns over the period 2012-2020 to analyze the impact of the coronavirus on the volatility of gold returns. Findings: We find a positive correlation between the increasing number of global coronavirus cases and increases in gold price. Using GARCH and GJR-GARCH models, we find a significant positive impact of COVID-19 on the conditional variance equation, indicating that the coronavirus may indeed increase the volatility of gold returns. This relates to the fact that the spread of the virus increases uncertainty with regard to the future of economic and financial markets, causing the demand for gold to increase and in turn pushing prices upwards, a trend which may be likely to continue until a vaccine or other treatments begin to stabilize the global economic outlook. Practical Implications: The issue of volatility is of significant concern to both investors and policymakers who base decisions on the relative stability of both individual financial markets and the world economy. Furthermore, volatility estimation is an essential factor in many models and has broad application to the market risk management practices of firms. Finally, understanding the volatility of the gold market is crucial for any analysis of current and future expectations regarding the risks associated with coronavirus which apply to global markets. Originality/Value: The lockdown restrictions which have been widely implemented across the globe to curb the spread of the virus have included travel prohibitions and border closures, stay-at-home and work-from-home orders, and extensive business closures, all causing immense fallout for the global economy. In the current study, we analyze for the first time the impact of the coronavirus on gold spot prices by examining their correlation with the number of cumulative global cases and daily new cases.

Suggested Citation

  • Ibrahim Yousef & Esam Shehadeh, 2020. "The Impact of COVID-19 on Gold Price Volatility," International Journal of Economics & Business Administration (IJEBA), International Journal of Economics & Business Administration (IJEBA), vol. 0(4), pages 353-364.
    • Handle: RePEc:ers:ijebaa:v:viii:y:2020:i:4:p:353-364
    as

    Download full text from publisher

    File URL: https://ijeba.com/journal/592/download
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Glosten, Lawrence R & Jagannathan, Ravi & Runkle, David E, 1993. "On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks," Journal of Finance, American Finance Association, vol. 48(5), pages 1779-1801, December.
    2. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    3. Claudiu Albulescu, 2020. "Coronavirus and oil price crash," Papers 2003.06184, arXiv.org, revised Mar 2020.
    4. Sadorsky, Perry, 2006. "Modeling and forecasting petroleum futures volatility," Energy Economics, Elsevier, vol. 28(4), pages 467-488, July.
    5. Jarque, Carlos M. & Bera, Anil K., 1980. "Efficient tests for normality, homoscedasticity and serial independence of regression residuals," Economics Letters, Elsevier, vol. 6(3), pages 255-259.
    6. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Herjuna Qobush Izzahdi & Ani Wilujeng Suryani, 2023. "COVID-19 Vaccination, Government Strict Policy and Capital Market Volatility: Evidence from ASEAN Countries," Economic Studies journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 2, pages 117-135.
    2. Tsai, I-Chun & Chen, Han-Bo & Lin, Che-Chun, 2024. "The ability of energy commodities to hedge the dynamic risk of epidemic black swans," Resources Policy, Elsevier, vol. 89(C).
    3. Xu, Qingqing & Meng, Tianci & Sha, Yue & Jiang, Xia, 2022. "Volatility in metallic resources prices in COVID-19 and financial Crises-2008: Evidence from global market," Resources Policy, Elsevier, vol. 78(C).
    4. Jing Niu & Chao Ma & Chun-Ping Chang, 2023. "The arbitrage strategy in the crude oil futures market of shanghai international energy exchange," Economic Change and Restructuring, Springer, vol. 56(2), pages 1201-1223, April.
    5. Yang, Mo & Wang, Ruotong & Zeng, Zixun & Li, Peizhi, 2024. "Improved prediction of global gold prices: An innovative Hurst-reconfiguration-based machine learning approach," Resources Policy, Elsevier, vol. 88(C).
    6. Atri, Hanen & Kouki, Saoussen & Gallali, Mohamed imen, 2021. "The impact of COVID-19 news, panic and media coverage on the oil and gold prices: An ARDL approach," Resources Policy, Elsevier, vol. 72(C).
    7. Muhammad Saeed & Ijaz Ahmad & Muhammad Ahmad Usman, 2021. "Do the stocks' returns and volatility matter under the COVID-19 pandemic? A Case Study of Pakistan Stock Exchange," iRASD Journal of Economics, International Research Alliance for Sustainable Development (iRASD), vol. 3(1), pages 13-26, june.
    8. Pierdomenico Duttilo & Stefano Antonio Gattone & Tonio Di Battista, 2021. "Volatility Modeling: An Overview of Equity Markets in the Euro Area during COVID-19 Pandemic," Mathematics, MDPI, vol. 9(11), pages 1-18, May.

    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. Halkos, George E. & Tsirivis, Apostolos S., 2019. "Effective energy commodity risk management: Econometric modeling of price volatility," Economic Analysis and Policy, Elsevier, vol. 63(C), pages 234-250.
    2. Dimitrios Kartsonakis-Mademlis & Nikolaos Dritsakis, 2020. "Does the Choice of the Multivariate GARCH Model on Volatility Spillovers Matter? Evidence from Oil Prices and Stock Markets in G7 Countries," International Journal of Energy Economics and Policy, Econjournals, vol. 10(5), pages 164-182.
    3. Wang, Yudong & Wu, Chongfeng & Yang, Li, 2016. "Forecasting crude oil market volatility: A Markov switching multifractal volatility approach," International Journal of Forecasting, Elsevier, vol. 32(1), pages 1-9.
    4. Wang, Yudong & Wu, Chongfeng, 2012. "Forecasting energy market volatility using GARCH models: Can multivariate models beat univariate models?," Energy Economics, Elsevier, vol. 34(6), pages 2167-2181.
    5. Wang, Yudong & Wu, Chongfeng & Wei, Yu, 2011. "Can GARCH-class models capture long memory in WTI crude oil markets?," Economic Modelling, Elsevier, vol. 28(3), pages 921-927, May.
    6. Dang, Tam Hoang Nhat & Balli, Faruk & Balli, Hatice Ozer & Gabauer, David & Nguyen, Thi Thu Ha, 2024. "Sectoral uncertainty spillovers in emerging markets: A quantile time–frequency connectedness approach," International Review of Economics & Finance, Elsevier, vol. 93(PB), pages 121-139.
    7. Efimova, Olga & Serletis, Apostolos, 2014. "Energy markets volatility modelling using GARCH," Energy Economics, Elsevier, vol. 43(C), pages 264-273.
    8. Chen, Hongtao & Liu, Li & Li, Xiaolei, 2018. "The predictive content of CBOE crude oil volatility index," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 492(C), pages 837-850.
    9. Li, Gang & Li, Yong, 2015. "Forecasting copper futures volatility under model uncertainty," Resources Policy, Elsevier, vol. 46(P2), pages 167-176.
    10. Zhang, Yue-Jun & Yao, Ting & He, Ling-Yun & Ripple, Ronald, 2019. "Volatility forecasting of crude oil market: Can the regime switching GARCH model beat the single-regime GARCH models?," International Review of Economics & Finance, Elsevier, vol. 59(C), pages 302-317.
    11. Lin, Xiaoqiang & Chen, Qiang & Tang, Zhenpeng, 2014. "Dynamic hedging strategy in incomplete market: Evidence from Shanghai fuel oil futures market," Economic Modelling, Elsevier, vol. 40(C), pages 81-90.
    12. Rosenberg, Joshua V. & Engle, Robert F., 2002. "Empirical pricing kernels," Journal of Financial Economics, Elsevier, vol. 64(3), pages 341-372, June.
    13. Güngör, Bekir Oray & Ertuğrul, H. Murat & Soytaş, Uğur, 2021. "Impact of Covid-19 outbreak on Turkish gasoline consumption," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    14. Kui-Wai Li, 2012. "A study on the volatility forecast of the US housing market in the 2008 crisis," Applied Financial Economics, Taylor & Francis Journals, vol. 22(22), pages 1869-1880, November.
    15. Hasanov, Akram Shavkatovich & Burkhanov, Aktam Usmanovich & Usmonov, Bunyod & Khajimuratov, Nizomjon Shukurullaevich & Khurramova, Madina Mansur qizi, 2024. "The role of sudden variance shifts in predicting volatility in bioenergy crop markets under structural breaks," Energy, Elsevier, vol. 293(C).
    16. Sadorsky, Perry, 2006. "Modeling and forecasting petroleum futures volatility," Energy Economics, Elsevier, vol. 28(4), pages 467-488, July.
    17. Wang, Yudong & Liu, Li & Ma, Feng & Wu, Chongfeng, 2016. "What the investors need to know about forecasting oil futures return volatility," Energy Economics, Elsevier, vol. 57(C), pages 128-139.
    18. Halkos, George & Tzirivis, Apostolos, 2018. "Effective energy commodities’ risk management: Econometric modeling of price volatility," MPRA Paper 90781, University Library of Munich, Germany.
    19. Arouri, Mohamed El Hédi & Lahiani, Amine & Lévy, Aldo & Nguyen, Duc Khuong, 2012. "Forecasting the conditional volatility of oil spot and futures prices with structural breaks and long memory models," Energy Economics, Elsevier, vol. 34(1), pages 283-293.
    20. Tarek Bouazizi & Mongi Lassoued & Zouhaier Hadhek, 2021. "Oil Price Volatility Models during Coronavirus Crisis: Testing with Appropriate Models Using Further Univariate GARCH and Monte Carlo Simulation Models," International Journal of Energy Economics and Policy, Econjournals, vol. 11(1), pages 281-292.

    More about this item

    Keywords

    Coronavirus; gold price; gold volatility; GARCH models.;
    All these keywords.

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

    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:ers:ijebaa:v:viii:y:2020:i:4:p:353-364. 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: Marios Agiomavritis (email available below). General contact details of provider: https://ijeba.com/ .

    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.