IDEAS home Printed from https://ideas.repec.org/p/pre/wpaper/202241.html
   My bibliography  Save this paper

Climate Risks and Predictability of the Trading Volume of Gold: Evidence from an INGARCH Model

Author

Listed:
  • Sayar Karmakar

    (Department of Statistics, University of Florida, 230 Newell Drive, Gainesville, FL, 32601, USA)

  • Rangan Gupta

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

  • Oguzhan Cepni

    (Copenhagen Business School, Department of Economics, Porcel16A, Frederiksberg DK-2000, Denmark; Central Bank of the Republic of Turkey, Haci Bayram Mah. Istiklal Cad. No:10 06050, Ankara, Turkey)

  • Lavinia Rognone

    (Alliance Manchester Business School, The University of Manchester, Booth St W, Manchester M15 6PB, UK)

Abstract

We investigate the ability of textual analysis-based metrics of physical or transition risks associated with climate change in forecasting the daily volume of trade contracts of gold. Given the count-valued nature of gold volume data, our econometric framework is a loglinear Poisson integer-valued generalized autoregressive conditional heteroskedasticity (INGARCH) model with a particular climate change-related covariate. We detect a significant predictive power for gold volume at 5- and 22-day-ahead horizons when we extend our model using physical risks. Given the underlying positively evolving impact of such risks on the trading volume of gold, as derived from a full-sample analysis using a time-varying INGARCH model, we can say that gold acts as a hedge against physical risks at 1-week and 1-month horizons. Such a characteristic is also detected for platinum, and to a lesser extent, for palladium, but not silver. Our results have important investment implications.

Suggested Citation

  • Sayar Karmakar & Rangan Gupta & Oguzhan Cepni & Lavinia Rognone, 2022. "Climate Risks and Predictability of the Trading Volume of Gold: Evidence from an INGARCH Model," Working Papers 202241, University of Pretoria, Department of Economics.
    • Handle: RePEc:pre:wpaper:202241
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Brian M. Lucey & Sile Li, 2015. "What precious metals act as safe havens, and when? Some US evidence," Applied Economics Letters, Taylor & Francis Journals, vol. 22(1), pages 35-45, January.
    2. Robert F Engle & Stefano Giglio & Bryan Kelly & Heebum Lee & Johannes Stroebel, 2020. "Hedging Climate Change News," The Review of Financial Studies, Society for Financial Studies, vol. 33(3), pages 1184-1216.
    3. Bua, Giovanna & Kapp, Daniel & Ramella, Federico & Rognone, Lavinia, 2022. "Transition versus physical climate risk pricing in European financial markets: a text-based approach," Working Paper Series 2677, European Central Bank.
    4. Cepni, Oguzhan & Demirer, Riza & Rognone, Lavinia, 2022. "Hedging climate risks with green assets," Economics Letters, Elsevier, vol. 212(C).
    5. Bonato, Matteo & Cepni, Oguzhan & Gupta, Rangan & Pierdzioch, Christian, 2023. "Climate risks and realized volatility of major commodity currency exchange rates," Journal of Financial Markets, Elsevier, vol. 62(C).
    6. George H. K. Wang & Jot Yau, 2000. "Trading volume, bid–ask spread, and price volatility in futures markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 20(10), pages 943-970, November.
    7. John Y. Campbell, 2008. "Viewpoint: Estimating the equity premium," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 41(1), pages 1-21, February.
    8. John Y. Campbell, 2007. "Estimating the Equity Premium," NBER Working Papers 13423, National Bureau of Economic Research, Inc.
    9. Gupta, Rangan & Pierdzioch, Christian, 2022. "Climate risks and forecastability of the realized volatility of gold and other metal prices," Resources Policy, Elsevier, vol. 77(C).
    10. 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.
    11. Boubaker, Heni & Cunado, Juncal & Gil-Alana, Luis A. & Gupta, Rangan, 2020. "Global crises and gold as a safe haven: Evidence from over seven and a half centuries of data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 540(C).
    12. Jonathan Andrew Batten & Brian M. Lucey, 2010. "Volatility in the gold futures market," Applied Economics Letters, Taylor & Francis Journals, vol. 17(2), pages 187-190, January.
    13. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    14. Flori, Andrea & Pammolli, Fabio & Spelta, Alessandro, 2021. "Commodity prices co-movements and financial stability: A multidimensional visibility nexus with climate conditions," Journal of Financial Stability, Elsevier, vol. 54(C).
    15. Bouri, Elie & Gupta, Rangan & Nel, Jacobus & Shiba, Sisa, 2022. "Contagious diseases and gold: Over 700 years of evidence from quantile regressions," Finance Research Letters, Elsevier, vol. 50(C).
    16. Afees A. Salisu & Rangan Gupta & Siphesihle Ntyikwe & Riza Demirer, 2021. "Gold and the Global Financial Cycle," Working Papers 202129, University of Pretoria, Department of Economics.
    17. Richard A. Davis & Konstantinos Fokianos & Scott H. Holan & Harry Joe & James Livsey & Robert Lund & Vladas Pipiras & Nalini Ravishanker, 2021. "Count Time Series: A Methodological Review," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 116(535), pages 1533-1547, May.
    18. Karmakar, Sayar & Richter, Stefan & Wu, Wei Biao, 2022. "Simultaneous inference for time-varying models," Journal of Econometrics, Elsevier, vol. 227(2), pages 408-428.
    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. Sun, Yiqun & Ji, Hao & Cai, Xiurong & Li, Jiangchen, 2023. "Joint extreme risk of energy prices-evidence from European energy markets," Finance Research Letters, Elsevier, vol. 56(C).
    2. Rangan Gupta & Anandamayee Majumdar & Christian Pierdzioch & Onur Polat, 2024. "Climate Risks and Real Gold Returns over 750 Years," Forecasting, MDPI, vol. 6(4), pages 1-16, October.
    3. Fava, Santino Del & Gupta, Rangan & Pierdzioch, Christian & Rognone, Lavinia, 2024. "Forecasting international financial stress: The role of climate risks," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 92(C).
    4. Kejin Wu & Sayar Karmakar & Rangan Gupta, 2023. "GARCHX-NoVaS: A Model-free Approach to Incorporate Exogenous Variables," Papers 2308.13346, arXiv.org, revised Sep 2024.

    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. Rangan Gupta & Anandamayee Majumdar & Christian Pierdzioch & Onur Polat, 2024. "Climate Risks and Real Gold Returns over 750 Years," Forecasting, MDPI, vol. 6(4), pages 1-16, October.
    2. Gupta, Rangan & Pierdzioch, Christian, 2022. "Climate risks and forecastability of the realized volatility of gold and other metal prices," Resources Policy, Elsevier, vol. 77(C).
    3. Fava, Santino Del & Gupta, Rangan & Pierdzioch, Christian & Rognone, Lavinia, 2024. "Forecasting international financial stress: The role of climate risks," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 92(C).
    4. Bonato, Matteo & Cepni, Oguzhan & Gupta, Rangan & Pierdzioch, Christian, 2023. "Climate risks and state-level stock market realized volatility," Journal of Financial Markets, Elsevier, vol. 66(C).
    5. Pham, Linh & Kamal, Javed Bin, 2024. "Blessings or curse: How do media climate change concerns affect commodity tail risk spillovers?," Journal of Commodity Markets, Elsevier, vol. 34(C).
    6. Bonato, Matteo & Cepni, Oguzhan & Gupta, Rangan & Pierdzioch, Christian, 2023. "Climate risks and realized volatility of major commodity currency exchange rates," Journal of Financial Markets, Elsevier, vol. 62(C).
    7. Rangan Gupta & Christian Pierdzioch, 2021. "Climate Risks and the Realized Volatility Oil and Gas Prices: Results of an Out-of-Sample Forecasting Experiment," Energies, MDPI, vol. 14(23), pages 1-18, December.
    8. Afees A. Salisu & Christian Pierdzioch & Rangan Gupta & Reneé van Eyden, 2023. "Climate risks and U.S. stock‐market tail risks: A forecasting experiment using over a century of data," International Review of Finance, International Review of Finance Ltd., vol. 23(2), pages 228-244, June.
    9. 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.
    10. Afees A. Salisu & Rangan Gupta & Ahamuefula E. Ogbonna, 2023. "Tail risks and forecastability of stock returns of advanced economies: evidence from centuries of data," The European Journal of Finance, Taylor & Francis Journals, vol. 29(4), pages 466-481, March.
    11. Mehmet Balcilar & Elie Bouri & Rangan Gupta & Christian Pierdzioch, 2021. "El Niño, La Niña, and the Forecastability of the Realized Variance of Heating Oil Price Movements," Sustainability, MDPI, vol. 13(14), pages 1-23, July.
    12. Salisu, Afees A. & Adekunle, Wasiu & Alimi, Wasiu A. & Emmanuel, Zachariah, 2019. "Predicting exchange rate with commodity prices: New evidence from Westerlund and Narayan (2015) estimator with structural breaks and asymmetries," Resources Policy, Elsevier, vol. 62(C), pages 33-56.
    13. Jiqian Wang & Rangan Gupta & Oğuzhan Çepni & Feng Ma, 2023. "Forecasting international REITs volatility: the role of oil-price uncertainty," The European Journal of Finance, Taylor & Francis Journals, vol. 29(14), pages 1579-1597, September.
    14. Bouri, Elie & Gkillas, Konstantinos & Gupta, Rangan & Pierdzioch, Christian, 2021. "Forecasting power of infectious diseases-related uncertainty for gold realized variance," Finance Research Letters, Elsevier, vol. 42(C).
    15. Rangan Gupta & Qiang Ji & Christian Pierdzioch, 2024. "Climate Policy Uncertainty and Financial Stress: Evidence for China," Working Papers 202428, University of Pretoria, Department of Economics.
    16. Chen, Zhonglu & Zhang, Li & Weng, Chen, 2023. "Does climate policy uncertainty affect Chinese stock market volatility?," International Review of Economics & Finance, Elsevier, vol. 84(C), pages 369-381.
    17. Bonato, Matteo & Çepni, Oğuzhan & Gupta, Rangan & Pierdzioch, Christian, 2021. "Do oil-price shocks predict the realized variance of U.S. REITs?," Energy Economics, Elsevier, vol. 104(C).
    18. Plakandaras, Vasilios & Gupta, Rangan & Wong, Wing-Keung, 2019. "Point and density forecasts of oil returns: The role of geopolitical risks," Resources Policy, Elsevier, vol. 62(C), pages 580-587.
    19. Baetje, Fabian & Menkhoff, Lukas, 2016. "Equity premium prediction: Are economic and technical indicators unstable?," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1193-1207.
    20. Karim, Sitara & Naeem, Muhammad Abubakr & Shafiullah, Muhammad & Lucey, Brian M. & Ashraf, Sania, 2023. "Asymmetric relationship between climate policy uncertainty and energy metals: Evidence from cross-quantilogram," Finance Research Letters, Elsevier, vol. 54(C).

    More about this item

    Keywords

    Climate Risks; Precious Metals; Forecasting; Trading Volumes; Count Data; INGARCH;
    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
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:pre:wpaper:202241. 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: Rangan Gupta (email available below). General contact details of provider: https://edirc.repec.org/data/decupza.html .

    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.