IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v557y2020ics0378437120304581.html
   My bibliography  Save this article

Dynamic dependence between ETFs and crude oil prices by using EGARCH-Copula approach

Author

Listed:
  • Naeem, Muhammad
  • Umar, Zaghum
  • Ahmed, Sheraz
  • Ferrouhi, El Mehdi

Abstract

In this study, we examine the average and extreme dependence between Exchange Traded Funds ETFs (both energy & commodity) and WTI crude oil prices by using EGARCH-copula models. We use both static (Normal, Student-t, Gumbel and Clayton) and time-varying (Normal and SJC) copulas to explore both average and extreme dependence. Based on the Akaike information criterion (AIC), our results show that time-varying copulas outperform the static copulas. Further, we have found strong enough positive correlations of energy and commodity ETFs with oil prices to suggest that they could be used as a tool for managing oil price risk. Also, contrasting results of time-varying copulas with each other provide useful information regarding the hedge or safe-haven properties of energy and commodity ETFs.

Suggested Citation

  • Naeem, Muhammad & Umar, Zaghum & Ahmed, Sheraz & Ferrouhi, El Mehdi, 2020. "Dynamic dependence between ETFs and crude oil prices by using EGARCH-Copula approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 557(C).
    • Handle: RePEc:eee:phsmap:v:557:y:2020:i:c:s0378437120304581
    DOI: 10.1016/j.physa.2020.124885
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437120304581
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2020.124885?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. Jiang, Yonghong & Lao, Jiashun & Mo, Bin & Nie, He, 2018. "Dynamic linkages among global oil market, agricultural raw material markets and metal markets: An application of wavelet and copula approaches," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 508(C), pages 265-279.
    2. Naeem, Muhammad & Shahbaz, Muhammad & Saleem, Kashif & Mustafa, Faisal, 2019. "Risk analysis of high frequency precious metals returns by using long memory model," Resources Policy, Elsevier, vol. 61(C), pages 399-409.
    3. Singhal, Shelly & Choudhary, Sangita & Biswal, Pratap Chandra, 2019. "Return and volatility linkages among International crude oil price, gold price, exchange rate and stock markets: Evidence from Mexico," Resources Policy, Elsevier, vol. 60(C), pages 255-261.
    4. Ji, Qiang & Liu, Bing-Yue & Fan, Ying, 2019. "Risk dependence of CoVaR and structural change between oil prices and exchange rates: A time-varying copula model," Energy Economics, Elsevier, vol. 77(C), pages 80-92.
    5. Charfeddine, Lanouar & Benlagha, Noureddine, 2016. "A time-varying copula approach for modelling dependency: New evidence from commodity and stock markets," Journal of Multinational Financial Management, Elsevier, vol. 37, pages 168-189.
    6. Muhammad Naeem & Hao Ji & Brunero Liseo, 2014. "Negative Return-Volume Relationship in Asian Stock Markets: Figarch-Copula Approach," Eurasian Journal of Economics and Finance, Eurasian Publications, vol. 2(2), pages 1-20.
    7. Lyócsa, Štefan & Molnár, Peter, 2018. "Exploiting dependence: Day-ahead volatility forecasting for crude oil and natural gas exchange-traded funds," Energy, Elsevier, vol. 155(C), pages 462-473.
    8. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    9. 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.
    10. Kang, Sang Hoon & Cheong, Chongcheul & Yoon, Seong-Min, 2010. "Long memory volatility in Chinese stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(7), pages 1425-1433.
    11. Jiang, Yonghong & Jiang, Cheng & Nie, He & Mo, Bin, 2019. "The time-varying linkages between global oil market and China's commodity sectors: Evidence from DCC-GJR-GARCH analyses," Energy, Elsevier, vol. 166(C), pages 577-586.
    12. 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.
    13. Jain, Anshul & Biswal, P.C., 2016. "Dynamic linkages among oil price, gold price, exchange rate, and stock market in India," Resources Policy, Elsevier, vol. 49(C), pages 179-185.
    14. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    15. Shahzad, Syed Jawad Hussain & Hernandez, Jose Arreola & Al-Yahyaee, Khamis Hamed & Jammazi, Rania, 2018. "Asymmetric risk spillovers between oil and agricultural commodities," Energy Policy, Elsevier, vol. 118(C), pages 182-198.
    16. Ma, Yiqun & Wang, Junhao, 2019. "Co-movement between oil, gas, coal, and iron ore prices, the Australian dollar, and the Chinese RMB exchange rates: A copula approach," Resources Policy, Elsevier, vol. 63(C), pages 1-1.
    17. Aloui, Riadh & Ben Aïssa, Mohamed Safouane & Nguyen, Duc Khuong, 2013. "Conditional dependence structure between oil prices and exchange rates: A copula-GARCH approach," Journal of International Money and Finance, Elsevier, vol. 32(C), pages 719-738.
    18. Caginalp, Gunduz & DeSantis, Mark & Sayrak, Akin, 2014. "The nonlinear price dynamics of U.S. equity ETFs," Journal of Econometrics, Elsevier, vol. 183(2), pages 193-201.
    19. 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.
    20. Srichander Ramaswamy, 2011. "Market structures and systemic risks of exchange-traded funds," BIS Working Papers 343, Bank for International Settlements.
    21. Krause, Timothy & Tse, Yiuman, 2013. "Volatility and return spillovers in Canadian and U.S. industry ETFs," International Review of Economics & Finance, Elsevier, vol. 25(C), pages 244-259.
    22. Tiwari, Aviral Kumar & Albulescu, Claudiu Tiberiu & Yoon, Seong-Min, 2017. "A multifractal detrended fluctuation analysis of financial market efficiency: Comparison using Dow Jones sector ETF indices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 483(C), pages 182-192.
    23. Reboredo, Juan C., 2013. "Is gold a hedge or safe haven against oil price movements?," Resources Policy, Elsevier, vol. 38(2), pages 130-137.
    24. Bollerslev, Tim, 1987. "A Conditionally Heteroskedastic Time Series Model for Speculative Prices and Rates of Return," The Review of Economics and Statistics, MIT Press, vol. 69(3), pages 542-547, August.
    25. Mensi, Walid & Tiwari, Aviral & Bouri, Elie & Roubaud, David & Al-Yahyaee, Khamis H., 2017. "The dependence structure across oil, wheat, and corn: A wavelet-based copula approach using implied volatility indexes," Energy Economics, Elsevier, vol. 66(C), pages 122-139.
    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. Katarzyna Kuziak & Joanna Górka, 2023. "Dependence Analysis for the Energy Sector Based on Energy ETFs," Energies, MDPI, vol. 16(3), pages 1-30, January.
    2. Mutaju Isaack Marobhe & Jonathan Mukiza Peter Kansheba, 2023. "High frequency volatility spillover between oil and non-energy commodities during crisis and tranquil periods," SN Business & Economics, Springer, vol. 3(4), pages 1-27, April.
    3. Aharon, David Y. & Umar, Zaghum & Aziz, Mukhriz Izraf Azman & Vo, Xuan vinh, 2022. "COVID-19 related media sentiment and the yield curve of G-7 economies," The North American Journal of Economics and Finance, Elsevier, vol. 61(C).
    4. Umar, Zaghum & Kenourgios, Dimitris & Papathanasiou, Sypros, 2020. "The static and dynamic connectedness of environmental, social, and governance investments: International evidence," Economic Modelling, Elsevier, vol. 93(C), pages 112-124.
    5. Umar, Zaghum & Usman, Muhammad & Choi, Sun-Yong & Rice, John, 2023. "Diversification benefits of NFTs for conventional asset investors: Evidence from CoVaR with higher moments and optimal hedge ratios," Research in International Business and Finance, Elsevier, vol. 65(C).
    6. Afees A. Salisu & Abdulsalam Abidemi Sikiru & Philip C. Omoke, 2023. "COVID-19 pandemic and financial innovations," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(4), pages 3885-3904, August.
    7. Çelik, İsmail & Sak, Ahmet Furkan & Höl, Arife Özdemir & Vergili, Gizem, 2022. "The dynamic connectedness and hedging opportunities of implied and realized volatility: Evidence from clean energy ETFs," The North American Journal of Economics and Finance, Elsevier, vol. 60(C).
    8. Umar, Zaghum & Jareño, Francisco & Escribano, Ana, 2021. "Agricultural commodity markets and oil prices: An analysis of the dynamic return and volatility connectedness," Resources Policy, Elsevier, vol. 73(C).
    9. James Ming Chen & Mobeen Ur Rehman, 2021. "A Pattern New in Every Moment: The Temporal Clustering of Markets for Crude Oil, Refined Fuels, and Other Commodities," Energies, MDPI, vol. 14(19), pages 1-58, September.
    10. Afees A. Salisu & Kingsley Obiora, 2021. "COVID-19 pandemic and the crude oil market risk: hedging options with non-energy financial innovations," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-19, December.
    11. Ghosh, Bikramaditya & Pham, Linh & Teplova, Tamara & Umar, Zaghum, 2023. "COVID-19 and the quantile connectedness between energy and metal markets," Energy Economics, Elsevier, vol. 117(C).
    12. Umar, Zaghum & Riaz, Yasir & Aharon, David Y., 2022. "Network connectedness dynamics of the yield curve of G7 countries," International Review of Economics & Finance, Elsevier, vol. 79(C), pages 275-288.
    13. Umar, Zaghum & Abrar, Afsheen & Hadhri, Sinda & Sokolova, Tatiana, 2023. "The connectedness of oil shocks, green bonds, sukuks and conventional bonds," Energy Economics, Elsevier, vol. 119(C).
    14. Umar, Zaghum & Gubareva, Mariya, 2020. "A time–frequency analysis of the impact of the Covid-19 induced panic on the volatility of currency and cryptocurrency markets," Journal of Behavioral and Experimental Finance, Elsevier, vol. 28(C).
    15. Umar, Zaghum & Gubareva, Mariya & Tran, Dang Khoa & Teplova, Tamara, 2021. "Impact of the Covid-19 induced panic on the Environmental, Social and Governance leaders equity volatility: A time-frequency analysis," Research in International Business and Finance, Elsevier, vol. 58(C).
    16. Umar, Zaghum & Aharon, David Y. & Esparcia, Carlos & AlWahedi, Wafa, 2022. "Spillovers between sovereign yield curve components and oil price shocks," Energy Economics, Elsevier, vol. 109(C).
    17. Umar, Zaghum & Gubareva, Mariya & Teplova, Tamara, 2021. "The impact of Covid-19 on commodity markets volatility: Analyzing time-frequency relations between commodity prices and coronavirus panic levels," Resources Policy, Elsevier, vol. 73(C).
    18. Alomari, Mohammad & Mensi, Walid & Vo, Xuan Vinh & Kang, Sang Hoon, 2022. "Extreme return spillovers and connectedness between crude oil and precious metals futures markets: Implications for portfolio management," Resources Policy, Elsevier, vol. 79(C).
    19. Umar, Zaghum & Jareño, Francisco & Escribano, Ana, 2021. "Oil price shocks and the return and volatility spillover between industrial and precious metals," Energy Economics, Elsevier, vol. 99(C).
    20. Adeleke, Musefiu A. & Awodumi, Olabanji B. & Adewuyi, Adeolu O., 2022. "Return and volatility connectedness among commodity markets during major crises periods: Static and dynamic analyses with asymmetries," Resources Policy, Elsevier, vol. 79(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. Tiwari, Aviral Kumar & Aye, Goodness C. & Gupta, Rangan & Gkillas, Konstantinos, 2020. "Gold-oil dependence dynamics and the role of geopolitical risks: Evidence from a Markov-switching time-varying copula model," Energy Economics, Elsevier, vol. 88(C).
    2. 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.
    3. Tim Bollerslev, 2008. "Glossary to ARCH (GARCH)," CREATES Research Papers 2008-49, Department of Economics and Business Economics, Aarhus University.
    4. Justice Matarutse, 2014. "Volatility characteristics of stocks underlying Exchange Traded Funds in South Africa," Journal of Economics and Behavioral Studies, AMH International, vol. 6(10), pages 829-839.
    5. Das, Suman & Roy, Saikat Sinha, 2023. "Following the leaders? A study of co-movement and volatility spillover in BRICS currencies," Economic Systems, Elsevier, vol. 47(2).
    6. Audrone Virbickaite & M. Concepción Ausín & Pedro Galeano, 2015. "Bayesian Inference Methods For Univariate And Multivariate Garch Models: A Survey," Journal of Economic Surveys, Wiley Blackwell, vol. 29(1), pages 76-96, February.
    7. Zhou, Wei & Chen, Yan & Chen, Jin, 2022. "Risk spread in multiple energy markets: Extreme volatility spillover network analysis before and during the COVID-19 pandemic," Energy, Elsevier, vol. 256(C).
    8. Mendoza-Velázquez, Alfonso & Galvanovskis, Evalds, 2009. "Introducing the GED-Copula with an application to Financial Contagion in Latin America," MPRA Paper 46669, University Library of Munich, Germany, revised 01 Feb 2010.
    9. Harvey,Andrew C., 2013. "Dynamic Models for Volatility and Heavy Tails," Cambridge Books, Cambridge University Press, number 9781107630024, January.
    10. Jiang, Yonghong & Jiang, Cheng & Nie, He & Mo, Bin, 2019. "The time-varying linkages between global oil market and China's commodity sectors: Evidence from DCC-GJR-GARCH analyses," Energy, Elsevier, vol. 166(C), pages 577-586.
    11. Lu Yang & Jason Z. Ma & Shigeyuki Hamori, 2018. "Dependence Structures and Systemic Risk of Government Securities Markets in Central and Eastern Europe: A CoVaR-Copula Approach," Sustainability, MDPI, vol. 10(2), pages 1-23, January.
    12. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    13. 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.
    14. Naeem, Muhammad & Tiwari, Aviral Kumar & Mubashra, Sana & Shahbaz, Muhammad, 2019. "Modeling volatility of precious metals markets by using regime-switching GARCH models," Resources Policy, Elsevier, vol. 64(C).
    15. Altaf Muhammad & Zhang Shuguang, 2015. "Impact Of Structural Shifts on Variance Persistence in Asymmetric Garch Models: Evidence From Emerging Asian and European Markets," Romanian Statistical Review, Romanian Statistical Review, vol. 63(1), pages 57-70, March.
    16. Lars Stentoft, 2008. "American Option Pricing Using GARCH Models and the Normal Inverse Gaussian Distribution," Journal of Financial Econometrics, Oxford University Press, vol. 6(4), pages 540-582, Fall.
    17. Luc Bauwens & Sébastien Laurent & Jeroen V. K. Rombouts, 2006. "Multivariate GARCH models: a survey," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(1), pages 79-109, January.
    18. Issler, João Victor, 1999. "Estimating and forecasting the volatility of Brazilian finance series using arch models (Preliminary Version)," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 347, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).
    19. Turan Bali & Panayiotis Theodossiou, 2007. "A conditional-SGT-VaR approach with alternative GARCH models," Annals of Operations Research, Springer, vol. 151(1), pages 241-267, April.
    20. Muhammad Sheraz & Imran Nasir, 2021. "Information-Theoretic Measures and Modeling Stock Market Volatility: A Comparative Approach," Risks, MDPI, vol. 9(5), pages 1-20, May.

    More about this item

    Keywords

    Crude-oil prices; Dependence; EGARCH; Time-varying copula; ETFs;
    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
    • C46 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Specific Distributions
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy

    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:eee:phsmap:v:557:y:2020:i:c:s0378437120304581. 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.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    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.