IDEAS home Printed from https://ideas.repec.org/a/eee/eneeco/v76y2018icp115-126.html
   My bibliography  Save this article

Uncertainties and extreme risk spillover in the energy markets: A time-varying copula-based CoVaR approach

Author

Listed:
  • Ji, Qiang
  • Liu, Bing-Yue
  • Nehler, Henrik
  • Uddin, Gazi Salah

Abstract

In this paper, we explore the impact of uncertainties on energy prices by measuring four types of Delta Conditional Value-at-Risk (∆CoVaR) using six time-varying copulas. Three different measures of uncertainty (economic policy, financial markets and energy markets) are considered, and the magnitude and asymmetric effects of their influence are investigated. Our results suggest that there generally exists negative dependence between energy returns and changes in uncertainty. The risks of clean energy and crude oil returns are more sensitive to uncertainties in the financial and energy markets, while the impact of economic policy uncertainty is relatively weak. The upside and downside CoVaRs and ∆CoVaRs demonstrate significant asymmetric effects in response to extreme uncertainty movement. Our findings therefore have important implications for energy portfolio investment.

Suggested Citation

  • Ji, Qiang & Liu, Bing-Yue & Nehler, Henrik & Uddin, Gazi Salah, 2018. "Uncertainties and extreme risk spillover in the energy markets: A time-varying copula-based CoVaR approach," Energy Economics, Elsevier, vol. 76(C), pages 115-126.
  • Handle: RePEc:eee:eneeco:v:76:y:2018:i:c:p:115-126
    DOI: 10.1016/j.eneco.2018.10.010
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0140988318304067
    Download Restriction: Full text for ScienceDirect subscribers only

    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. Changqing Luo & Mengzhen Li & Zisheng Ouyang, 2016. "An Empirical Study on the Correlation Structure of Credit Spreads based on the Dynamic and Pair Copula Functions," China Finance Review International, Emerald Group Publishing, vol. 6(3), pages 284-303, August.
    2. N. Bloom., 2016. "Fluctuations in uncertainty," VOPROSY ECONOMIKI, N.P. Redaktsiya zhurnala "Voprosy Economiki", vol. 4.
    3. Mellios, Constantin & Six, Pierre & Lai, Anh Ngoc, 2016. "Dynamic speculation and hedging in commodity futures markets with a stochastic convenience yield," European Journal of Operational Research, Elsevier, vol. 250(2), pages 493-504.
    4. Mensi, Walid & Hammoudeh, Shawkat & Shahzad, Syed Jawad Hussain & Shahbaz, Muhammad, 2017. "Modeling systemic risk and dependence structure between oil and stock markets using a variational mode decomposition-based copula method," Journal of Banking & Finance, Elsevier, vol. 75(C), pages 258-279.
    5. Bekaert, Geert & Hoerova, Marie & Lo Duca, Marco, 2013. "Risk, uncertainty and monetary policy," Journal of Monetary Economics, Elsevier, vol. 60(7), pages 771-788.
    6. Fernandes, Marcelo & Medeiros, Marcelo C. & Scharth, Marcel, 2014. "Modeling and predicting the CBOE market volatility index," Journal of Banking & Finance, Elsevier, vol. 40(C), pages 1-10.
    7. R?diger Bachmann & Steffen Elstner & Eric R. Sims, 2013. "Uncertainty and Economic Activity: Evidence from Business Survey Data," American Economic Journal: Macroeconomics, American Economic Association, vol. 5(2), pages 217-249, April.
    8. Diebold, Francis X. & Yılmaz, Kamil, 2014. "On the network topology of variance decompositions: Measuring the connectedness of financial firms," Journal of Econometrics, Elsevier, vol. 182(1), pages 119-134.
    9. Yao Zheng, 2014. "The linkage between aggregate stock market investor sentiment and commodity futures returns," Applied Financial Economics, Taylor & Francis Journals, vol. 24(23), pages 1491-1513, December.
    10. Antonakakis, Nikolaos & Floros, Christos, 2016. "Dynamic interdependencies among the housing market, stock market, policy uncertainty and the macroeconomy in the United Kingdom," International Review of Financial Analysis, Elsevier, vol. 44(C), pages 111-122.
    11. Patton, Andrew J., 2012. "A review of copula models for economic time series," Journal of Multivariate Analysis, Elsevier, vol. 110(C), pages 4-18.
    12. Scott R. Baker & Nicholas Bloom & Steven J. Davis, 2016. "Measuring Economic Policy Uncertainty," The Quarterly Journal of Economics, Oxford University Press, vol. 131(4), pages 1593-1636.
    13. Kyle Jurado & Sydney C. Ludvigson & Serena Ng, 2015. "Measuring Uncertainty," American Economic Review, American Economic Association, vol. 105(3), pages 1177-1216, March.
    14. Mehmet Huseyin Bilgin & Giray Gozgor & Gokhan Karabulut, 2015. "The Impact Of World Energy Price Volatility On Aggregate Economic Activity In Developing Asian Economies," The Singapore Economic Review (SER), World Scientific Publishing Co. Pte. Ltd., vol. 60(01), pages 1-20.
    15. repec:eee:pacfin:v:46:y:2017:i:pa:p:41-56 is not listed on IDEAS
    16. Mingyuan Guo & Xu Wang, 2016. "The dependence structure in volatility between Shanghai and Shenzhen Stock Market in China: A copula-MEM approach," China Finance Review International, Emerald Group Publishing, vol. 6(3), pages 264-283, August.
    17. Libo Yin & Liyan Han, 2014. "Macroeconomic uncertainty: does it matter for commodity prices?," Applied Economics Letters, Taylor & Francis Journals, vol. 21(10), pages 711-716, July.
    18. repec:eee:eneeco:v:75:y:2018:i:c:p:14-27 is not listed on IDEAS
    19. Aloui, Riadh & Gupta, Rangan & Miller, Stephen M., 2016. "Uncertainty and crude oil returns," Energy Economics, Elsevier, vol. 55(C), pages 92-100.
    20. Pástor, Ľuboš & Veronesi, Pietro, 2013. "Political uncertainty and risk premia," Journal of Financial Economics, Elsevier, vol. 110(3), pages 520-545.
    21. Agbeyegbe, Terence D., 2015. "An inverted U-shaped crude oil price return-implied volatility relationship," Review of Financial Economics, Elsevier, vol. 27(C), pages 28-45.
    22. repec:eee:jimfin:v:76:y:2017:i:c:p:50-67 is not listed on IDEAS
    23. Aboura, Sofiane & Chevallier, Julien, 2013. "Leverage vs. feedback: Which Effect drives the oil market?," Finance Research Letters, Elsevier, vol. 10(3), pages 131-141.
    24. Mele, Antonio & Obayashi, Yoshiki & Shalen, Catherine, 2015. "Rate fears gauges and the dynamics of fixed income and equity volatilities," Journal of Banking & Finance, Elsevier, vol. 52(C), pages 256-265.
    25. Chen, Liang & Kettunen, Janne, 2017. "Is certainty in carbon policy better than uncertainty?," European Journal of Operational Research, Elsevier, vol. 258(1), pages 230-243.
    26. Zhiyuan Pan & Xu Zheng & Qiang Chen, 2014. "Testing asymmetric correlations in stock returns via empirical likelihood method," China Finance Review International, Emerald Group Publishing, vol. 4(1), pages 42-57, February.
    27. Bekiros, Stelios & Nguyen, Duc Khuong & Sandoval Junior, Leonidas & Uddin, Gazi Salah, 2017. "Information diffusion, cluster formation and entropy-based network dynamics in equity and commodity markets," European Journal of Operational Research, Elsevier, vol. 256(3), pages 945-961.
    28. Nicholas Bloom & Ian Wright & Jose Maria Barrero, 2016. "Short- and Long-run Uncertainty," 2016 Meeting Papers 1576, Society for Economic Dynamics.
    29. Ji, Qiang & Fan, Ying, 2016. "Modelling the joint dynamics of oil prices and investor fear gauge," Research in International Business and Finance, Elsevier, vol. 37(C), pages 242-251.
    30. Zhang, Dayong, 2017. "Oil shocks and stock markets revisited: Measuring connectedness from a global perspective," Energy Economics, Elsevier, vol. 62(C), pages 323-333.
    31. Girardi, Giulio & Tolga Ergün, A., 2013. "Systemic risk measurement: Multivariate GARCH estimation of CoVaR," Journal of Banking & Finance, Elsevier, vol. 37(8), pages 3169-3180.
    32. repec:eee:ememar:v:33:y:2017:i:c:p:189-200 is not listed on IDEAS
    33. Reboredo, Juan C. & Rivera-Castro, Miguel A. & Ugolini, Andrea, 2016. "Downside and upside risk spillovers between exchange rates and stock prices," Journal of Banking & Finance, Elsevier, vol. 62(C), pages 76-96.
    34. Barry Nalebuff & David Scharfstein, 1987. "Testing in Models of Asymmetric Information," Review of Economic Studies, Oxford University Press, vol. 54(2), pages 265-277.
    35. repec:eee:eneeco:v:68:y:2017:i:c:p:53-65 is not listed on IDEAS
    36. Wisniewski, Tomasz Piotr & Lambe, Brendan John, 2015. "Does economic policy uncertainty drive CDS spreads?," International Review of Financial Analysis, Elsevier, vol. 42(C), pages 447-458.
    37. Mingyuan Guo & Xu Wang & Gongmeng Chen & Yudong Wang, 2016. "The dependence structure in volatility between Shanghai and Shenzhen Stock Market in China: A copula-MEM approach," China Finance Review International, Emerald Group Publishing, vol. 6(3), pages 1-1, October.
    38. 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.
    39. repec:taf:quantf:v:17:y:2017:i:3:p:437-453 is not listed on IDEAS
    40. repec:dau:papers:123456789/9860 is not listed on IDEAS
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    Uncertainty; Time-varying copula; ∆CoVaR; Extreme risk;

    JEL classification:

    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • 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:eee:eneeco:v:76:y:2018:i:c:p:115-126. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Dana Niculescu). General contact details of provider: http://www.elsevier.com/locate/eneco .

    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 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.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.