IDEAS home Printed from https://ideas.repec.org/a/eee/jocoma/v28y2022ics2405851321000696.html
   My bibliography  Save this article

Linkage transitions between oil and the stock markets of countries with the highest COVID-19 cases

Author

Listed:
  • Hussain, Saiful Izzuan
  • Nur-Firyal, R.
  • Ruza, Nadiah

Abstract

This paper employed dynamic copulas and Extreme Value Theory (EVT) to analyze the linkages between oil and the stock market of countries with the highest number of COVID-19 cases. Many papers have reported small but significant negative dependence between oil and the markets before the pandemic. This work enhances the understanding of these links by exploring the tail behavior. There are many extreme returns during these periods. Integration between copula with EVT does help to understand these extreme returns better. The linkages for an overall dependence structure began to decrease dramatically and volatile after the pandemic outbreak. Lower dependence for most pairs seems to be stronger most of the time during COVID-19. The research results have possible effects on portfolio and risk management and provide insights that could be used to assess the long- and short-term effects of the pandemic.

Suggested Citation

  • Hussain, Saiful Izzuan & Nur-Firyal, R. & Ruza, Nadiah, 2022. "Linkage transitions between oil and the stock markets of countries with the highest COVID-19 cases," Journal of Commodity Markets, Elsevier, vol. 28(C).
    • Handle: RePEc:eee:jocoma:v:28:y:2022:i:c:s2405851321000696
    DOI: 10.1016/j.jcomm.2021.100236
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S2405851321000696
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jcomm.2021.100236?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Chen, Xiaohong & Fan, Yanqin, 2006. "Estimation of copula-based semiparametric time series models," Journal of Econometrics, Elsevier, vol. 130(2), pages 307-335, February.
    2. Annastiina Silvennoinen & Timo Teräsvirta, 2009. "Modeling Multivariate Autoregressive Conditional Heteroskedasticity with the Double Smooth Transition Conditional Correlation GARCH Model," Journal of Financial Econometrics, Oxford University Press, vol. 7(4), pages 373-411, Fall.
    3. Massimiliano Caporin & Michael McAleer, 2013. "Ten Things You Should Know about the Dynamic Conditional Correlation Representation," Econometrics, MDPI, vol. 1(1), pages 1-12, June.
    4. Engle, Robert F & Ng, Victor K, 1993. "Measuring and Testing the Impact of News on Volatility," Journal of Finance, American Finance Association, vol. 48(5), pages 1749-1778, December.
    5. Baek, Seungho & Mohanty, Sunil K. & Glambosky, Mina, 2020. "COVID-19 and stock market volatility: An industry level analysis," Finance Research Letters, Elsevier, vol. 37(C).
    6. Lutz Kilian & Cheolbeom Park, 2009. "The Impact Of Oil Price Shocks On The U.S. Stock Market," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 50(4), pages 1267-1287, November.
    7. Saiful Izzuan Hussain & Steven Li, 2018. "The dynamic dependence between stock markets in the greater China economic area: a study based on extreme values and copulas," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 32(2), pages 207-233, May.
    8. Donia Aloui & Stéphane Goutte & Khaled Guesmi & Rafla Hchaichi, 2020. "COVID 19's impact on crude oil and natural gas S&P GS Indexes," Working Papers halshs-02613280, HAL.
    9. Nurulkamal Masseran & Saiful Izzuan Hussain, 2020. "Copula Modelling on the Dynamic Dependence Structure of Multiple Air Pollutant Variables," Mathematics, MDPI, vol. 8(11), pages 1-15, October.
    10. Geman, Hélyette & Kharoubi, Cécile, 2008. "WTI crude oil Futures in portfolio diversification: The time-to-maturity effect," Journal of Banking & Finance, Elsevier, vol. 32(12), pages 2553-2559, December.
    11. Martín-Barragán, Belén & Ramos, Sofia B. & Veiga, Helena, 2015. "Correlations between oil and stock markets: A wavelet-based approach," Economic Modelling, Elsevier, vol. 50(C), pages 212-227.
    12. Wen, Xiaoqian & Wei, Yu & Huang, Dengshi, 2012. "Measuring contagion between energy market and stock market during financial crisis: A copula approach," Energy Economics, Elsevier, vol. 34(5), pages 1435-1446.
    13. Garcia-Jorcano, Laura & Benito, Sonia, 2020. "Studying the properties of the Bitcoin as a diversifying and hedging asset through a copula analysis: Constant and time-varying," Research in International Business and Finance, Elsevier, vol. 54(C).
    14. Salisu, Afees A. & Ebuh, Godday U. & Usman, Nuruddeen, 2020. "Revisiting oil-stock nexus during COVID-19 pandemic: Some preliminary results," International Review of Economics & Finance, Elsevier, vol. 69(C), pages 280-294.
    15. Xiao, Yang, 2020. "The risk spillovers from the Chinese stock market to major East Asian stock markets: A MSGARCH-EVT-copula approach," International Review of Economics & Finance, Elsevier, vol. 65(C), pages 173-186.
    16. Du, Limin & He, Yanan, 2015. "Extreme risk spillovers between crude oil and stock markets," Energy Economics, Elsevier, vol. 51(C), pages 455-465.
    17. Ser-Huang Poon, 2004. "Extreme Value Dependence in Financial Markets: Diagnostics, Models, and Financial Implications," The Review of Financial Studies, Society for Financial Studies, vol. 17(2), pages 581-610.
    18. Andrew J. Patton, 2006. "Estimation of multivariate models for time series of possibly different lengths," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(2), pages 147-173, March.
    19. Ding, Haoyuan & Kim, Hyung-Gun & Park, Sung Y., 2016. "Crude oil and stock markets: Causal relationships in tails?," Energy Economics, Elsevier, vol. 59(C), pages 58-69.
    20. McNeil, Alexander J. & Frey, Rudiger, 2000. "Estimation of tail-related risk measures for heteroscedastic financial time series: an extreme value approach," Journal of Empirical Finance, Elsevier, vol. 7(3-4), pages 271-300, November.
    21. Zhang, Wenting & Hamori, Shigeyuki, 2021. "Crude oil market and stock markets during the COVID-19 pandemic: Evidence from the US, Japan, and Germany," International Review of Financial Analysis, Elsevier, vol. 74(C).
    22. Bhatti, M. Ishaq & Nguyen, Cuong C., 2012. "Diversification evidence from international equity markets using extreme values and stochastic copulas," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 22(3), pages 622-646.
    23. Engle, Robert, 2002. "Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 339-350, July.
    24. Georgios Bampinas & Theodore Panagiotidis, 2017. "Oil and stock markets before and after financial crises: A local Gaussian correlation approach," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 37(12), pages 1179-1204, December.
    25. Andrew J. Patton, 2006. "Modelling Asymmetric Exchange Rate Dependence," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 47(2), pages 527-556, May.
    26. Bampinas Georgios & Panagiotidis Theodore, 2015. "On the relationship between oil and gold before and after financial crisis: linear, nonlinear and time-varying causality testing," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 19(5), pages 657-668, December.
    27. François Longin & Bruno Solnik, 2001. "Extreme Correlation of International Equity Markets," Journal of Finance, American Finance Association, vol. 56(2), pages 649-676, April.
    28. Longin, Francois M, 1996. "The Asymptotic Distribution of Extreme Stock Market Returns," The Journal of Business, University of Chicago Press, vol. 69(3), pages 383-408, July.
    29. Sharif, Arshian & Aloui, Chaker & Yarovaya, Larisa, 2020. "COVID-19 pandemic, oil prices, stock market, geopolitical risk and policy uncertainty nexus in the US economy: Fresh evidence from the wavelet-based approach," International Review of Financial Analysis, Elsevier, vol. 70(C).
    30. Rashmi Chaudhary & Priti Bakhshi & Hemendra Gupta, 2020. "Volatility in International Stock Markets: An Empirical Study during COVID-19," JRFM, MDPI, vol. 13(9), pages 1-17, September.
    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. Bouazizi, Tarek & Abid, Ilyes & Guesmi, Khaled & Makrychoriti, Panagiota, 2024. "Evolving energies: Analyzing stability amidst recent challenges in the natural gas market," International Review of Financial Analysis, Elsevier, vol. 95(PA).
    2. Zhao, Lu-Tao & Liu, Hai-Yi & Chen, Xue-Hui, 2024. "How does carbon market interact with energy and sectoral stocks? Evidence from risk spillover and wavelet coherence," Journal of Commodity Markets, Elsevier, vol. 33(C).

    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. Hussain, Saiful Izzuan & Li, Steven, 2018. "The dependence structure between Chinese and other major stock markets using extreme values and copulas," International Review of Economics & Finance, Elsevier, vol. 56(C), pages 421-437.
    2. Saiful Izzuan Hussain & Steven Li, 2018. "The dynamic dependence between stock markets in the greater China economic area: a study based on extreme values and copulas," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 32(2), pages 207-233, May.
    3. Ji, Hao & Naeem, Muhammad & Zhang, Jing & Tiwari, Aviral Kumar, 2024. "Dynamic dependence and spillover among the energy related ETFs: From the hedging effectiveness perspective," Energy Economics, Elsevier, vol. 136(C).
    4. Andersen, Torben G. & Bollerslev, Tim & Christoffersen, Peter F. & Diebold, Francis X., 2013. "Financial Risk Measurement for Financial Risk Management," Handbook of the Economics of Finance, in: G.M. Constantinides & M. Harris & R. M. Stulz (ed.), Handbook of the Economics of Finance, volume 2, chapter 0, pages 1127-1220, Elsevier.
    5. Avdulaj, Krenar & Barunik, Jozef, 2015. "Are benefits from oil–stocks diversification gone? New evidence from a dynamic copula and high frequency data," Energy Economics, Elsevier, vol. 51(C), pages 31-44.
    6. Tong, Bin & Wu, Chongfeng & Zhou, Chunyang, 2013. "Modeling the co-movements between crude oil and refined petroleum markets," Energy Economics, Elsevier, vol. 40(C), pages 882-897.
    7. Mensi, Walid & Rehman, Mobeen Ur & Al-Yahyaee, Khamis Hamed & Vo, Xuan Vinh, 2023. "Frequency dependence between oil futures and international stock markets and the role of gold, bonds, and uncertainty indices: Evidence from partial and multivariate wavelet approaches," Resources Policy, Elsevier, vol. 80(C).
    8. Sim, Nicholas, 2016. "Modeling the dependence structures of financial assets through the Copula Quantile-on-Quantile approach," International Review of Financial Analysis, Elsevier, vol. 48(C), pages 31-45.
    9. Nurulkamal Masseran & Saiful Izzuan Hussain, 2020. "Copula Modelling on the Dynamic Dependence Structure of Multiple Air Pollutant Variables," Mathematics, MDPI, vol. 8(11), pages 1-15, October.
    10. Georgios Bampinas & Theodore Panagiotidis, 2017. "Oil and stock markets before and after financial crises: A local Gaussian correlation approach," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 37(12), pages 1179-1204, December.
    11. Guoxiang Xu & Wangfeng Gao, 2019. "Financial Risk Contagion in Stock Markets: Causality and Measurement Aspects," Sustainability, MDPI, vol. 11(5), pages 1-20, March.
    12. Martin Hoesli & Kustrim Reka, 2013. "Volatility Spillovers, Comovements and Contagion in Securitized Real Estate Markets," The Journal of Real Estate Finance and Economics, Springer, vol. 47(1), pages 1-35, July.
    13. Christoffersen, Peter & Langlois, Hugues, 2013. "The Joint Dynamics of Equity Market Factors," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 48(5), pages 1371-1404, October.
    14. Chen, Yi-Hsuan & Tu, Anthony H., 2013. "Estimating hedged portfolio value-at-risk using the conditional copula: An illustration of model risk," International Review of Economics & Finance, Elsevier, vol. 27(C), pages 514-528.
    15. Mensah, Jones Odei & Premaratne, Gamini, 2014. "Dependence patterns among Banking Sectors in Asia: A Copula Approach," MPRA Paper 60119, University Library of Munich, Germany.
    16. Wang, Kehluh & Chen, Yi-Hsuan & Huang, Szu-Wei, 2011. "The dynamic dependence between the Chinese market and other international stock markets: A time-varying copula approach," International Review of Economics & Finance, Elsevier, vol. 20(4), pages 654-664, October.
    17. Tong, Bin & Diao, Xundi & Wu, Chongfeng, 2015. "Modeling asymmetric and dynamic dependence of overnight and daytime returns: An empirical evidence from China Banking Sector," Economic Modelling, Elsevier, vol. 51(C), pages 366-382.
    18. Mensi, Walid & Selmi, Refk & Al-Yahyaee, Khamis Hamed, 2020. "Switching dependence and systemic risk between crude oil and U.S. Islamic and conventional equity markets: A new evidence," Resources Policy, Elsevier, vol. 69(C).
    19. Siburg, Karl Friedrich & Stoimenov, Pavel & Weiß, Gregor N.F., 2015. "Forecasting portfolio-Value-at-Risk with nonparametric lower tail dependence estimates," Journal of Banking & Finance, Elsevier, vol. 54(C), pages 129-140.
    20. Tian, Maoxi & Alshater, Muneer M. & Yoon, Seong-Min, 2022. "Dynamic risk spillovers from oil to stock markets: Fresh evidence from GARCH copula quantile regression-based CoVaR model," Energy Economics, Elsevier, vol. 115(C).

    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:eee:jocoma:v:28:y:2022:i:c:s2405851321000696. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/jcomm .

    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.