IDEAS home Printed from https://ideas.repec.org/a/eee/ecmode/v29y2012i6p2298-2309.html
   My bibliography  Save this article

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

Author

Listed:
  • 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.

Suggested Citation

  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0264999312001897
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.econmod.2012.06.016?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. Das, Sanjiv R., 2002. "The surprise element: jumps in interest rates," Journal of Econometrics, Elsevier, vol. 106(1), pages 27-65, January.
    2. Chiou, Jer-Shiou & Lee, Yen-Hsien, 2009. "Jump dynamics and volatility: Oil and the stock markets," Energy, Elsevier, vol. 34(6), pages 788-796.
    3. Daal, Elton & Naka, Atsuyuki & Yu, Jung-Suk, 2007. "Volatility clustering, leverage effects, and jump dynamics in the US and emerging Asian equity markets," Journal of Banking & Finance, Elsevier, vol. 31(9), pages 2751-2769, September.
    4. Fan, Ying & Zhang, Yue-Jun & Tsai, Hsien-Tang & Wei, Yi-Ming, 2008. "Estimating 'Value at Risk' of crude oil price and its spillover effect using the GED-GARCH approach," Energy Economics, Elsevier, vol. 30(6), pages 3156-3171, November.
    5. 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.
    6. Lai, YiHao & Chen, Cathy W.S. & Gerlach, Richard, 2009. "Optimal dynamic hedging via copula-threshold-GARCH models," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(8), pages 2609-2624.
    7. Lien, Donald & Yang, Li, 2008. "Asymmetric effect of basis on dynamic futures hedging: Empirical evidence from commodity markets," Journal of Banking & Finance, Elsevier, vol. 32(2), pages 187-198, February.
    8. Chan, Wing H & Maheu, John M, 2002. "Conditional Jump Dynamics in Stock Market Returns," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 377-389, July.
    9. Agnolucci, Paolo, 2009. "Volatility in crude oil futures: A comparison of the predictive ability of GARCH and implied volatility models," Energy Economics, Elsevier, vol. 31(2), pages 316-321, March.
    10. Johansen, Soren, 1995. "Likelihood-Based Inference in Cointegrated Vector Autoregressive Models," OUP Catalogue, Oxford University Press, number 9780198774501.
    11. 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.
    12. Giot, Pierre & Laurent, Sebastien, 2003. "Market risk in commodity markets: a VaR approach," Energy Economics, Elsevier, vol. 25(5), pages 435-457, September.
    13. Chen, Shyh-Wei & Shen, Chung-Hua, 2004. "GARCH, jumps and permanent and transitory components of volatility: the case of the Taiwan exchange rate," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 67(3), pages 201-216.
    14. John M. Maheu & Thomas H. McCurdy, 2004. "News Arrival, Jump Dynamics, and Volatility Components for Individual Stock Returns," Journal of Finance, American Finance Association, vol. 59(2), pages 755-793, April.
    15. Alizadeh, Amir H. & Nomikos, Nikos K. & Pouliasis, Panos K., 2008. "A Markov regime switching approach for hedging energy commodities," Journal of Banking & Finance, Elsevier, vol. 32(9), pages 1970-1983, September.
    16. Jondeau, Eric & Rockinger, Michael, 2006. "The Copula-GARCH model of conditional dependencies: An international stock market application," Journal of International Money and Finance, Elsevier, vol. 25(5), pages 827-853, August.
    17. Lee, Yen-Hsien & Hu, Hsu-Ning & Chiou, Jer-Shiou, 2010. "Jump dynamics with structural breaks for crude oil prices," Energy Economics, Elsevier, vol. 32(2), pages 343-350, March.
    18. Huang, Bwo-Nung & Yang, C.W. & Hwang, M.J., 2009. "The dynamics of a nonlinear relationship between crude oil spot and futures prices: A multivariate threshold regression approach," Energy Economics, Elsevier, vol. 31(1), pages 91-98, January.
    19. Chan Wing Hong, 2008. "Dynamic Hedging with Foreign Currency Futures in the Presence of Jumps," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 12(2), pages 1-25, May.
    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. Xiangjun Chen & Bo Yan, 2024. "Research on jumps and volatility in China’s carbon market," Economic Change and Restructuring, Springer, vol. 57(1), pages 1-43, February.
    2. Bouteska, Ahmed & Sharif, Taimur & Abedin, Mohammad Zoynul, 2023. "COVID-19 and stock returns: Evidence from the Markov switching dependence approach," Research in International Business and Finance, Elsevier, vol. 64(C).
    3. Narayan, Paresh Kumar & Phan, Dinh Hoang Bach & Thuraisamy, Kannan & Westerlund, Joakim, 2016. "Price discovery and asset pricing," Pacific-Basin Finance Journal, Elsevier, vol. 40(PA), pages 224-235.
    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. 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.

    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. 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.
    2. Chang, Kuang-Liang, 2012. "Volatility regimes, asymmetric basis effects and forecasting performance: An empirical investigation of the WTI crude oil futures market," Energy Economics, Elsevier, vol. 34(1), pages 294-306.
    3. Das, Debojyoti & Bhatia, Vaneet & Kumar, Surya Bhushan & Basu, Sankarshan, 2022. "Do precious metals hedge crude oil volatility jumps?," International Review of Financial Analysis, Elsevier, vol. 83(C).
    4. Liu, Feng & Zhang, Chuanguo & Tang, Mengying, 2021. "The impacts of oil price shocks and jumps on China's nonferrous metal markets," Resources Policy, Elsevier, vol. 73(C).
    5. Zhang, Chuanguo & Liu, Feng & Yu, Danlin, 2018. "Dynamic jumps in global oil price and its impacts on China's bulk commodities," Energy Economics, Elsevier, vol. 70(C), pages 297-306.
    6. Zhang, Chuanguo & Qu, Xuqin, 2015. "The effect of global oil price shocks on China's agricultural commodities," Energy Economics, Elsevier, vol. 51(C), pages 354-364.
    7. Liu, Feng & Shao, Shuai & Zhang, Chuanguo, 2020. "How do China's petrochemical markets react to oil price jumps? A comparative analysis of stocks and commodities," Energy Economics, Elsevier, vol. 92(C).
    8. Ghorbel, Ahmed & Trabelsi, Abdelwahed, 2014. "Energy portfolio risk management using time-varying extreme value copula methods," Economic Modelling, Elsevier, vol. 38(C), pages 470-485.
    9. Kang, Sang Hoon & Yoon, Seong-Min, 2013. "Modeling and forecasting the volatility of petroleum futures prices," Energy Economics, Elsevier, vol. 36(C), pages 354-362.
    10. Wang, Yudong & Wu, Chongfeng, 2012. "Forecasting energy market volatility using GARCH models: Can multivariate models beat univariate models?," Energy Economics, Elsevier, vol. 34(6), pages 2167-2181.
    11. Mason, Charles F. & Wilmot, Neil A., 2020. "Jumps in the convenience yield of crude oil," Resource and Energy Economics, Elsevier, vol. 60(C).
    12. Kuttu, Saint, 2017. "Time-varying conditional discrete jumps in emerging African equity markets," Global Finance Journal, Elsevier, vol. 32(C), pages 35-54.
    13. Wang, Xiao & Zhang, Chuanguo, 2014. "The impacts of global oil price shocks on China׳s fundamental industries," Energy Policy, Elsevier, vol. 68(C), pages 394-402.
    14. Chia-Hsun Hsieh & Shian-Chang Huang, 2012. "Time-Varying Dependency and Structural Changes in Currency Markets," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 48(2), pages 94-127, March.
    15. Sébastien Laurent & Luc Bauwens & Jeroen V. K. Rombouts, 2006. "Multivariate GARCH models: a survey," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(1), pages 79-109.
    16. Wan-Hsiu Cheng, 2008. "Overestimation in the Traditional GARCH Model During Jump Periods," Economics Bulletin, AccessEcon, vol. 3(68), pages 1-20.
    17. Bernardi, Mauro & Catania, Leopoldo, 2018. "Portfolio optimisation under flexible dynamic dependence modelling," Journal of Empirical Finance, Elsevier, vol. 48(C), pages 1-18.
    18. Rangel, José Gonzalo, 2011. "Macroeconomic news, announcements, and stock market jump intensity dynamics," Journal of Banking & Finance, Elsevier, vol. 35(5), pages 1263-1276, May.
    19. Sharon S. Yang & Jr-Wei Huang & Chuang-Chang Chang, 2016. "Detecting and modelling the jump risk of CO 2 emission allowances and their impact on the valuation of option on futures contracts," Quantitative Finance, Taylor & Francis Journals, vol. 16(5), pages 749-762, May.
    20. Rakesh Gupta & Junhao Yang & Thadavillil Jithendranathan, 2017. "Diversification into Emerging Markets – An Australian and the US Perspective Using a Time-varying Approach," Australian Economic Papers, Wiley Blackwell, vol. 56(2), pages 134-162, June.

    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

    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:ecmode:v:29:y:2012:i:6:p:2298-2309. 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.elsevier.com/locate/inca/30411 .

    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.