IDEAS home Printed from https://ideas.repec.org/p/zbw/safewp/324.html
   My bibliography  Save this paper

Time-varying granger causality tests for applications in global crude oil markets: A study on the DCC-MGARCH Hong test

Author

Listed:
  • Caporina, Massimiliano
  • Costola, Michele

Abstract

Analysing causality among oil prices and, in general, among financial and economic variables is of central relevance in applied economics studies. The recent contribution of Lu et al. (2014) proposes a novel test for causality- the DCC-MGARCH Hong test. We show that the critical values of the test statistic must be evaluated through simulations, thereby challenging the evidence in papers adopting the DCC-MGARCH Hong test. We also note that rolling Hong tests represent a more viable solution in the presence of short-lived causality periods.

Suggested Citation

  • Caporina, Massimiliano & Costola, Michele, 2021. "Time-varying granger causality tests for applications in global crude oil markets: A study on the DCC-MGARCH Hong test," SAFE Working Paper Series 324, Leibniz Institute for Financial Research SAFE.
  • Handle: RePEc:zbw:safewp:324
    DOI: 10.2139/ssrn.3941778
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/243283/1/safe-wp-324.pdf
    Download Restriction: no

    File URL: https://libkey.io/10.2139/ssrn.3941778?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. Kanda, Patrick & Burke, Michael & Gupta, Rangan, 2018. "Time-varying causality between equity and currency returns in the United Kingdom: Evidence from over two centuries of data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 1060-1080.
    2. Bathia, Deven & Demirer, Riza & Gupta, Rangan & Kotzé, Kevin, 2021. "Unemployment fluctuations and currency returns in the United Kingdom: Evidence from over one and a half century of data," Journal of Multinational Financial Management, Elsevier, vol. 61(C).
    3. Massimiliano Caporin & Michael McAleer, 2012. "Do We Really Need Both Bekk And Dcc? A Tale Of Two Multivariate Garch Models," Journal of Economic Surveys, Wiley Blackwell, vol. 26(4), pages 736-751, September.
    4. Lu, Feng-bin & Hong, Yong-miao & Wang, Shou-yang & Lai, Kin-keung & Liu, John, 2014. "Time-varying Granger causality tests for applications in global crude oil markets," Energy Economics, Elsevier, vol. 42(C), pages 289-298.
    5. Bekiros, Stelios D. & Diks, Cees G.H., 2008. "The relationship between crude oil spot and futures prices: Cointegration, linear and nonlinear causality," Energy Economics, Elsevier, vol. 30(5), pages 2673-2685, September.
    6. Hammoudeh, Shawkat & Li, Huimin, 2004. "The impact of the Asian crisis on the behavior of US and international petroleum prices," Energy Economics, Elsevier, vol. 26(1), pages 135-160, January.
    7. Hong, Yongmiao & Liu, Yanhui & Wang, Shouyang, 2009. "Granger causality in risk and detection of extreme risk spillover between financial markets," Journal of Econometrics, Elsevier, vol. 150(2), pages 271-287, June.
    8. Engle, Robert F & Sheppard, Kevin K, 2001. "Theoretical and Empirical Properties of Dynamic Conditional Correlation Multivariate GARCH," University of California at San Diego, Economics Working Paper Series qt5s2218dp, Department of Economics, UC San Diego.
    9. Hong, Yongmiao, 2001. "A test for volatility spillover with application to exchange rates," Journal of Econometrics, Elsevier, vol. 103(1-2), pages 183-224, July.
    10. Sibande, Xolani & Gupta, Rangan & Wohar, Mark E., 2019. "Time-varying causal relationship between stock market and unemployment in the United Kingdom: Historical evidence from 1855 to 2017," Journal of Multinational Financial Management, Elsevier, vol. 49(C), pages 81-88.
    11. Semei Coronado & Rangan Gupta & Besma Hkiri & Omar Rojas, 2020. "Time-Varying Spillovers between Currency and Stock Markets in the USA: Historical Evidence From More than Two Centuries," Advances in Decision Sciences, Asia University, Taiwan, vol. 24(4), pages 44-76, December.
    12. Lin, Sharon Xiaowen & Tamvakis, Michael N., 2004. "Effects of NYMEX trading on IPE Brent Crude futures markets: a duration analysis," Energy Policy, Elsevier, vol. 32(1), pages 77-82, January.
    13. 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.
    14. Cheung, Yin-Wong & Ng, Lilian K., 1996. "A causality-in-variance test and its application to financial market prices," Journal of Econometrics, Elsevier, vol. 72(1-2), pages 33-48.
    15. Gupta, Rangan & Kanda, Patrick & Tiwari, Aviral Kumar & Wohar, Mark E., 2019. "Time-varying predictability of oil market movements over a century of data: The role of US financial stress," The North American Journal of Economics and Finance, Elsevier, vol. 50(C).
    16. Xun Zhang & Fengbin Lu & Rui Tao & Shouyang Wang, 2021. "The time-varying causal relationship between the Bitcoin market and internet attention," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-19, December.
    17. Granger, C W J, 1969. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, Econometric Society, vol. 37(3), pages 424-438, July.
    18. Lin, Sharon Xiaowen & Tamvakis, Michael N., 2001. "Spillover effects in energy futures markets," Energy Economics, Elsevier, vol. 23(1), pages 43-56, January.
    19. Semei Coronado & Rangan Gupta & Besma Hkiri & Omar Rojas, 2020. "Time-Varying Spillover between Currency and Stock Markets in the United States: More than Two Centuries of Historical Evidence," Working Papers 202060, University of Pretoria, Department of Economics.
    20. Caporin, Massimiliano & Fontini, Fulvio & Talebbeydokhti, Elham, 2019. "Testing persistence of WTI and Brent long-run relationship after the shale oil supply shock," Energy Economics, Elsevier, vol. 79(C), pages 21-31.
    21. Geng, Jiang-Bo & Ji, Qiang & Fan, Ying, 2017. "The relationship between regional natural gas markets and crude oil markets from a multi-scale nonlinear Granger causality perspective," Energy Economics, Elsevier, vol. 67(C), pages 98-110.
    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. Caporin, Massimiliano & Costola, Michele, 2022. "Time-varying Granger causality tests in the energy markets: A study on the DCC-MGARCH Hong test," Energy Economics, Elsevier, vol. 111(C).
    2. Lu, Feng-bin & Hong, Yong-miao & Wang, Shou-yang & Lai, Kin-keung & Liu, John, 2014. "Time-varying Granger causality tests for applications in global crude oil markets," Energy Economics, Elsevier, vol. 42(C), pages 289-298.
    3. Bathia, Deven & Demirer, Riza & Gupta, Rangan & Kotzé, Kevin, 2021. "Unemployment fluctuations and currency returns in the United Kingdom: Evidence from over one and a half century of data," Journal of Multinational Financial Management, Elsevier, vol. 61(C).
    4. Katarzyna Kuziak & Joanna Górka, 2023. "Dependence Analysis for the Energy Sector Based on Energy ETFs," Energies, MDPI, vol. 16(3), pages 1-30, January.
    5. Wang, Gang-Jin & Xie, Chi & Jiang, Zhi-Qiang & Stanley, H. Eugene, 2016. "Extreme risk spillover effects in world gold markets and the global financial crisis," International Review of Economics & Finance, Elsevier, vol. 46(C), pages 55-77.
    6. Mensi, Walid & Rehman, Mobeen Ur & Vo, Xuan Vinh, 2021. "Dynamic frequency relationships and volatility spillovers in natural gas, crude oil, gas oil, gasoline, and heating oil markets: Implications for portfolio management," Resources Policy, Elsevier, vol. 73(C).
    7. Kuruppuarachchi, Duminda & Premachandra, I.M., 2016. "Information spillover dynamics of the energy futures market sector: A novel common factor approach," Energy Economics, Elsevier, vol. 57(C), pages 277-294.
    8. Jammazi, Rania & Ferrer, Román & Jareño, Francisco & Shahzad, Syed Jawad Hussain, 2017. "Time-varying causality between crude oil and stock markets: What can we learn from a multiscale perspective?," International Review of Economics & Finance, Elsevier, vol. 49(C), pages 453-483.
    9. Kanda, Patrick & Burke, Michael & Gupta, Rangan, 2018. "Time-varying causality between equity and currency returns in the United Kingdom: Evidence from over two centuries of data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 1060-1080.
    10. Jammazi, Rania & Ferrer, Román & Jareño, Francisco & Hammoudeh, Shawkat M., 2017. "Main driving factors of the interest rate-stock market Granger causality," International Review of Financial Analysis, Elsevier, vol. 52(C), pages 260-280.
    11. Bubák, Vít & Kocenda, Evzen & Zikes, Filip, 2011. "Volatility transmission in emerging European foreign exchange markets," Journal of Banking & Finance, Elsevier, vol. 35(11), pages 2829-2841, November.
    12. Soylu, Pınar Kaya & Güloğlu, Bülent, 2019. "Financial contagion and flight to quality between emerging markets and U.S. bond market," The North American Journal of Economics and Finance, Elsevier, vol. 50(C).
    13. Piero Mazzarisi & Silvia Zaoli & Carlo Campajola & Fabrizio Lillo, 2020. "Tail Granger causalities and where to find them: extreme risk spillovers vs. spurious linkages," Papers 2005.01160, arXiv.org, revised May 2021.
    14. Tomoaki Nakatani & Timo Terasvirta, 2009. "Testing for volatility interactions in the Constant Conditional Correlation GARCH model," Econometrics Journal, Royal Economic Society, vol. 12(1), pages 147-163, March.
    15. Gang-Jin Wang & Chi Xie & Kaijian He & H. Eugene Stanley, 2017. "Extreme risk spillover network: application to financial institutions," Quantitative Finance, Taylor & Francis Journals, vol. 17(9), pages 1417-1433, September.
    16. Výrost, Tomáš & Lyócsa, Štefan & Baumöhl, Eduard, 2015. "Granger causality stock market networks: Temporal proximity and preferential attachment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 427(C), pages 262-276.
    17. Semei Coronado & Jose N. Martinez & Victor Gualajara & Rafael Romero-Meza & Omar Rojas, 2023. "Time-Varying Granger Causality of COVID-19 News on Emerging Financial Markets: The Latin American Case," Mathematics, MDPI, vol. 11(2), pages 1-18, January.
    18. Mert Demir & Terrence F. Martell & Jun Wang, 2019. "The trilogy of China cotton markets: The lead–lag relationship among spot, forward, and futures markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 39(4), pages 522-534, April.
    19. Kitamura, Yoshihiro, 2010. "Testing for intraday interdependence and volatility spillover among the euro, the pound and the Swiss franc markets," Research in International Business and Finance, Elsevier, vol. 24(2), pages 158-171, June.
    20. Li, Haiqi & Zhong, Wanling & Park, Sung Y., 2016. "Generalized cross-spectral test for nonlinear Granger causality with applications to money–output and price–volume relations," Economic Modelling, Elsevier, vol. 52(PB), pages 661-671.

    More about this item

    Keywords

    Granger Causality; Hong test; DCC-GARCH; Oil market; COVID-19;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

    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:zbw:safewp:324. 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: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: https://edirc.repec.org/data/csafede.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.