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Measurement of risk spillover effect based on EV-Copula method

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  • Yuexu Zhao

    (Hangzhou Dianzi University)

  • Weiqi Xu

    (Hangzhou Dianzi University)

Abstract

Based on the extreme value theory, copula function, and conditional value at risk (Abbreviated as CoVaR) model, an extreme value copula CoVaR (EV-Copula CoVaR) model is established. In application, the risk spillover effect of the carbon trading market on the stock market of China is investigated. Firstly, using the index synthesis method, the carbon trading price index is synthesized through the price data of the test area of carbon emission, then the risk spillover effect of the carbon market is measured by the EV-Copula CoVaR, and the dynamic risk spillover ΔCoVaR of the carbon market to each stock market is investigated. Finally, the downside ΔCoVaR under different significance levels is measured, and the relationship between the self-risk and spillover risk of the carbon market is explored, the largest risk spillover effect to the stock market is the electricity market. The smaller the significance level, the greater the carbon market self-risk, and the greater the risk spillover of the carbon market to the stock market, which shows that there is a positive correlation between them.

Suggested Citation

  • Yuexu Zhao & Weiqi Xu, 2023. "Measurement of risk spillover effect based on EV-Copula method," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-10, December.
  • Handle: RePEc:pal:palcom:v:10:y:2023:i:1:d:10.1057_s41599-023-02287-5
    DOI: 10.1057/s41599-023-02287-5
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    References listed on IDEAS

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    1. Oberndorfer, Ulrich, 2008. "EU Emission Allowances and the Stock Market: Evidence from the Electricity Industry," ZEW Discussion Papers 08-059, ZEW - Leibniz Centre for European Economic Research.
    2. Azomahou, Theophile & Laisney, Francois & Nguyen Van, Phu, 2006. "Economic development and CO2 emissions: A nonparametric panel approach," Journal of Public Economics, Elsevier, vol. 90(6-7), pages 1347-1363, August.
    3. Masaaki Kijima & Akira Maeda & Katsumasa Nishide, 2010. "Equilibrium pricing of contingent claims in tradable permit markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 30(6), pages 559-589, June.
    4. James B. Bushnell & Howard Chong & Erin T. Mansur, 2013. "Profiting from Regulation: Evidence from the European Carbon Market," American Economic Journal: Economic Policy, American Economic Association, vol. 5(4), pages 78-106, November.
    5. Garcia, René & Tsafack, Georges, 2011. "Dependence structure and extreme comovements in international equity and bond markets," Journal of Banking & Finance, Elsevier, vol. 35(8), pages 1954-1970, August.
    6. Zhou, Yuqin & Wu, Shan & Zhang, Zeyi, 2022. "Multidimensional risk spillovers among carbon, energy and nonferrous metals markets: Evidence from the quantile VAR network," Energy Economics, Elsevier, vol. 114(C).
    7. Stöber, Jakob & Czado, Claudia, 2014. "Regime switches in the dependence structure of multidimensional financial data," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 672-686.
    8. Paul H. Kupiec, 1995. "Techniques for verifying the accuracy of risk measurement models," Finance and Economics Discussion Series 95-24, Board of Governors of the Federal Reserve System (U.S.).
    9. 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.
    10. BenSaïda, Ahmed, 2018. "The contagion effect in European sovereign debt markets: A regime-switching vine copula approach," International Review of Financial Analysis, Elsevier, vol. 58(C), pages 153-165.
    11. Meredith L. Fowlie & Mar Reguant, 2022. "Mitigating Emissions Leakage in Incomplete Carbon Markets," Journal of the Association of Environmental and Resource Economists, University of Chicago Press, vol. 9(2), pages 307-343.
    12. Gomez-Gonzalez, Jose E. & Rojas-Espinosa, Wilmer, 2019. "Detecting contagion in Asian exchange rate markets using asymmetric DCC-GARCH and R-vine copulas," Economic Systems, Elsevier, vol. 43(3).
    13. Yin, Libo & Feng, Jiabao & Han, Liyan, 2021. "Systemic risk in international stock markets: Role of the oil market," International Review of Economics & Finance, Elsevier, vol. 71(C), pages 592-619.
    14. Daskalakis, George & Markellos, Raphael N., 2009. "Are electricity risk premia affected by emission allowance prices? Evidence from the EEX, Nord Pool and Powernext," Energy Policy, Elsevier, vol. 37(7), pages 2594-2604, July.
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