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The time-varying and asymmetric dependence between crude oil spot and futures markets: Evidence from the Mixture copula-based ARJI–GARCH model

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  • Chang, Kuang-Liang

Abstract

This paper designs a Mixture copula-based ARJI–GARCH model to simultaneously investigate the dynamic process of crude oil spot and futures returns and the time-varying and asymmetric dependence between spot and futures returns. The individual behavior of each market is modeled by the ARJI–GARCH process. The time-varying and asymmetric dependence is captured by the Mixture copula which is composed of the Gumbel copula and Clayton copula. Empirical results show three important findings. First, jumping behavior is an important process for each market. Second, spot and futures returns do not have the same jump process. Third, the tail dependence between spot and futures markets is time-varying and asymmetric with the magnitude of upper tail dependence being slightly weaker than that of lower tail dependence.

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  • Chang, Kuang-Liang, 2012. "The time-varying and asymmetric dependence between crude oil spot and futures markets: Evidence from the Mixture copula-based ARJI–GARCH model," Economic Modelling, Elsevier, vol. 29(6), pages 2298-2309.
    • Handle: RePEc:eee:ecmode:v:29:y:2012:i:6:p:2298-2309
    DOI: 10.1016/j.econmod.2012.06.016
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    4. Guo, Yanfeng & Wen, Xiaoqian & Wu, Yanrui & Guo, Xiumei, 2016. "How is China's coke price related with the world oil price? The role of extreme movements," Economic Modelling, Elsevier, vol. 58(C), pages 22-33.
    5. Nguyen, Cuong & Bhatti, M. Ishaq & Komorníková, Magda & Komorník, Jozef, 2016. "Gold price and stock markets nexus under mixed-copulas," Economic Modelling, Elsevier, vol. 58(C), pages 283-292.
    6. Zolotko, Mikhail & Okhrin, Ostap, 2014. "Modelling the general dependence between commodity forward curves," Energy Economics, Elsevier, vol. 43(C), pages 284-296.
    7. Meng, Juan & Nie, He & Mo, Bin & Jiang, Yonghong, 2020. "Risk spillover effects from global crude oil market to China’s commodity sectors," Energy, Elsevier, vol. 202(C).
    8. Zhouwei Wang & Qicheng Zhao & Min Zhu & Tao Pang, 2020. "Jump Aggregation, Volatility Prediction, and Nonlinear Estimation of Banks’ Sustainability Risk," Sustainability, MDPI, vol. 12(21), pages 1-17, October.
    9. Zhu, Pengfei & Lu, Tuantuan & Chen, Shenglan, 2022. "How do crude oil futures hedge crude oil spot risk after the COVID-19 outbreak? A wavelet denoising-GARCHSK-SJC Copula hedge ratio estimation method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).
    10. Andrey Bedin & Alexander Kulikov & Andrey Polbin, 2023. "Copula-Based Modelling of Relationship Between Dollar/Rouble Exchange Rate and Oil Prices," Russian Journal of Money and Finance, Bank of Russia, vol. 82(3), pages 87-109, September.
    11. Lee, Chien-Chiang & Chen, Mei-Ping & Chang, Chi-Hung, 2014. "Industry co-movement and cross-listing: Do home country factors matter?," Japan and the World Economy, Elsevier, vol. 32(C), pages 96-110.
    12. Liu, Xiang-dong & Pan, Fei & Cai, Wen-li & Peng, Rui, 2020. "Correlation and risk measurement modeling: A Markov-switching mixed Clayton copula approach," Reliability Engineering and System Safety, Elsevier, vol. 197(C).
    13. Arthur Charpentier, 2015. "Prévision avec des copules en finance," Working Papers hal-01151233, HAL.
    14. Hemei Li & Zhenya Liu & Shixuan Wang, 2022. "Vines climbing higher: Risk management for commodity futures markets using a regular vine copula approach," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(2), pages 2438-2457, April.
    15. Kuang-Liang Chang, 2021. "A New Dynamic Mixture Copula Mechanism to Examine the Nonlinear and Asymmetric Tail Dependence Between Stock and Exchange Rate Returns," Computational Economics, Springer;Society for Computational Economics, vol. 58(4), pages 965-999, December.
    16. Abootaleb Shirvani & Dimitri Volchenkov, 2019. "A Regulated Market Under Sanctions: On Tail Dependence Between Oil, Gold, and Tehran Stock Exchange Index," Papers 1911.01826, arXiv.org.
    17. Chang, Kai & Zhang, Chao, 2018. "Asymmetric dependence structure between emissions allowances and wholesale diesel/gasoline prices in emerging China's emissions trading scheme pilots," Energy, Elsevier, vol. 164(C), pages 124-136.
    18. Paravee Maneejuk & Woraphon Yamaka, 2019. "Predicting Contagion from the US Financial Crisis to International Stock Markets Using Dynamic Copula with Google Trends," Mathematics, MDPI, vol. 7(11), pages 1-29, November.
    19. Li, Haiqi & Kim, Hyung-Gun & Park, Sung Y., 2015. "The role of financial speculation in the energy future markets: A new time-varying coefficient approach," Economic Modelling, Elsevier, vol. 51(C), pages 112-122.
    20. Kim, Jong-Min & Jung, Hojin, 2017. "Can asymmetric conditional volatility imply asymmetric tail dependence?," Economic Modelling, Elsevier, vol. 64(C), pages 409-418.
    21. Emmanuel Afuecheta & Saralees Nadarajah & Stephen Chan, 2021. "A Statistical Analysis of Global Economies Using Time Varying Copulas," Computational Economics, Springer;Society for Computational Economics, vol. 58(4), pages 1167-1194, December.
    22. Roberto Esposti, 2022. "Dating Common Commodity Price And Inflation Shocks With Alternative Approaches," Working Papers 469, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    23. Zolotko, Mikhail & Okhrin, Ostap, 2012. "Modelling general dependence between commodity forward curves," SFB 649 Discussion Papers 2012-060, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    24. EnDer Su, 2018. "Measuring contagion risk in high volatility state among Taiwanese major banks," Risk Management, Palgrave Macmillan, vol. 20(3), pages 185-241, August.

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    More about this item

    Keywords

    Jump process; Copula; Nonlinear dependence; Crude oil market;
    All these keywords.

    JEL classification:

    • 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
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General

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