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The dependence and risk spillover between crude oil market and China stock market: New evidence from a variational mode decomposition-based copula method

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  • Li, Xiafei
  • Wei, Yu

Abstract

This paper examines the dependence structure between crude oil market and China stock market over different investment horizons, before and after the recent financial crisis, by combining the variational mode decomposition (VMD) method with various static and time-varying copulas. Based on the decomposed time series and the copula dependence, the Value-at-Risk (VaR), conditional VaR (CoVaR) and delta CoVaR (ΔCoVaR) are quantified to analyze the upside and downside risk spillovers from oil market to China stock market in raw, short- and long-run investment horizons before and after the financial crisis. The empirical results show that, first, the recent financial crisis enhances the dependences between the crude oil market and China stock market, and the long-run dependence increases more significantly than that of short-run. For the raw return series, there are symmetric upper and lower tail dependencies in full sample and pre-crisis subsample periods, but an average dependence in post-crisis subsample period. Second, the VaR of China stock market increases heavily around the financial crisis, but the average VaR after the crisis deceases compared to the risk before the crisis. Third, the risk spillovers from crude oil price to China stock market are found in each sample periods. Before the crisis, however, it mainly exists in long-run horizon, while after the crisis, it happens in both short- and long-run horizons. Finally, the risk spillovers from oil price to China stock market display strong asymmetric features, with larger long-term, downside risk spillovers in post-crisis subsample.

Suggested Citation

  • Li, Xiafei & Wei, Yu, 2018. "The dependence and risk spillover between crude oil market and China stock market: New evidence from a variational mode decomposition-based copula method," Energy Economics, Elsevier, vol. 74(C), pages 565-581.
  • Handle: RePEc:eee:eneeco:v:74:y:2018:i:c:p:565-581
    DOI: 10.1016/j.eneco.2018.07.011
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    as
    1. Jiang, Yonghong & Lao, Jiashun & Mo, Bin & Nie, He, 2018. "Dynamic linkages among global oil market, agricultural raw material markets and metal markets: An application of wavelet and copula approaches," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 508(C), pages 265-279.
    2. Mohammadi, Hassan & Su, Lixian, 2010. "International evidence on crude oil price dynamics: Applications of ARIMA-GARCH models," Energy Economics, Elsevier, vol. 32(5), pages 1001-1008, September.
    3. Balcilar, Mehmet & Gupta, Rangan & Miller, Stephen M., 2015. "Regime switching model of US crude oil and stock market prices: 1859 to 2013," Energy Economics, Elsevier, vol. 49(C), pages 317-327.
    4. Yang, Liansheng & Zhu, Yingming & Wang, Yudong & Wang, Yiqi, 2016. "Multifractal detrended cross-correlations between crude oil market and Chinese ten sector stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 255-265.
    5. Hilde C. Bjørnland, 2009. "Oil Price Shocks And Stock Market Booms In An Oil Exporting Country," Scottish Journal of Political Economy, Scottish Economic Society, vol. 56(2), pages 232-254, May.
    6. Raza, Naveed & Jawad Hussain Shahzad, Syed & Tiwari, Aviral Kumar & Shahbaz, Muhammad, 2016. "Asymmetric impact of gold, oil prices and their volatilities on stock prices of emerging markets," Resources Policy, Elsevier, vol. 49(C), pages 290-301.
    7. Bernal, Oscar & Gnabo, Jean-Yves & Guilmin, Grégory, 2014. "Assessing the contribution of banks, insurance and other financial services to systemic risk," Journal of Banking & Finance, Elsevier, vol. 47(C), pages 270-287.
    8. Kang, Wensheng & Ratti, Ronald A. & Yoon, Kyung Hwan, 2015. "Time-varying effect of oil market shocks on the stock market," Journal of Banking & Finance, Elsevier, vol. 61(S2), pages 150-163.
    9. Papapetrou, Evangelia, 2001. "Oil price shocks, stock market, economic activity and employment in Greece," Energy Economics, Elsevier, vol. 23(5), pages 511-532, September.
    10. Liu, Xueyong & An, Haizhong & Huang, Shupei & Wen, Shaobo, 2017. "The evolution of spillover effects between oil and stock markets across multi-scales using a wavelet-based GARCH–BEKK model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 465(C), pages 374-383.
    11. Glosten, Lawrence R & Jagannathan, Ravi & Runkle, David E, 1993. "On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks," Journal of Finance, American Finance Association, vol. 48(5), pages 1779-1801, December.
    12. Avdulaj, Krenar & Barunik, Jozef, 2015. "Are benefits from oil–stocks diversification gone? New evidence from a dynamic copula and high frequency data," Energy Economics, Elsevier, vol. 51(C), pages 31-44.
    13. Apergis, Nicholas & Miller, Stephen M., 2009. "Do structural oil-market shocks affect stock prices?," Energy Economics, Elsevier, vol. 31(4), pages 569-575, July.
    14. Reboredo, Juan C., 2011. "How do crude oil prices co-move?: A copula approach," Energy Economics, Elsevier, vol. 33(5), pages 948-955, September.
    15. 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.
    16. Francois Chesnay & Eric Jondeau, 2001. "Does Correlation Between Stock Returns Really Increase During Turbulent Periods?," Economic Notes, Banca Monte dei Paschi di Siena SpA, vol. 30(1), pages 53-80, February.
    17. Lahmiri, Salim, 2015. "Long memory in international financial markets trends and short movements during 2008 financial crisis based on variational mode decomposition and detrended fluctuation analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 437(C), pages 130-138.
    18. Reboredo, Juan C. & Ugolini, Andrea, 2015. "Systemic risk in European sovereign debt markets: A CoVaR-copula approach," Journal of International Money and Finance, Elsevier, vol. 51(C), pages 214-244.
    19. Sadorsky, Perry, 2012. "Correlations and volatility spillovers between oil prices and the stock prices of clean energy and technology companies," Energy Economics, Elsevier, vol. 34(1), pages 248-255.
    20. Kee-Hong Bae & G. Andrew Karolyi & René M. Stulz, 2003. "A New Approach to Measuring Financial Contagion," The Review of Financial Studies, Society for Financial Studies, vol. 16(3), pages 717-763, July.
    21. Aloui, Riadh & Gupta, Rangan & Miller, Stephen M., 2016. "Uncertainty and crude oil returns," Energy Economics, Elsevier, vol. 55(C), pages 92-100.
    22. Miller, J. Isaac & Ratti, Ronald A., 2009. "Crude oil and stock markets: Stability, instability, and bubbles," Energy Economics, Elsevier, vol. 31(4), pages 559-568, July.
    23. Berger, Theo & Uddin, Gazi Salah, 2016. "On the dynamic dependence between equity markets, commodity futures and economic uncertainty indexes," Energy Economics, Elsevier, vol. 56(C), pages 374-383.
    24. Wen, Xiaoqian & Wei, Yu & Huang, Dengshi, 2012. "Measuring contagion between energy market and stock market during financial crisis: A copula approach," Energy Economics, Elsevier, vol. 34(5), pages 1435-1446.
    25. Silva Filho, Osvaldo Candido da & Ziegelmann, Flavio Augusto & Dueker, Michael J., 2012. "Modeling dependence dynamics through copulas with regime switching," Insurance: Mathematics and Economics, Elsevier, vol. 50(3), pages 346-356.
    26. Shahzad, Syed Jawad Hussain & Kumar, Ronald Ravinesh & Ali, Sajid & Ameer, Saba, 2016. "Interdependence between Greece and other European stock markets: A comparison of wavelet and VMD copula, and the portfolio implications," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 457(C), pages 8-33.
    27. Rodriguez, Juan Carlos, 2007. "Measuring financial contagion: A Copula approach," Journal of Empirical Finance, Elsevier, vol. 14(3), pages 401-423, June.
    28. Ser-Huang Poon, 2004. "Extreme Value Dependence in Financial Markets: Diagnostics, Models, and Financial Implications," Review of Financial Studies, Society for Financial Studies, vol. 17(2), pages 581-610.
    29. Chiou, Jer-Shiou & Lee, Yen-Hsien, 2009. "Jump dynamics and volatility: Oil and the stock markets," Energy, Elsevier, vol. 34(6), pages 788-796.
    30. Park, Jungwook & Ratti, Ronald A., 2008. "Oil price shocks and stock markets in the U.S. and 13 European countries," Energy Economics, Elsevier, vol. 30(5), pages 2587-2608, September.
    31. Hedi Arouri, Mohamed El & Khuong Nguyen, Duc, 2010. "Oil prices, stock markets and portfolio investment: Evidence from sector analysis in Europe over the last decade," Energy Policy, Elsevier, vol. 38(8), pages 4528-4539, August.
    32. Balcilar, Mehmet & Ozdemir, Zeynel Abidin, 2013. "The causal nexus between oil prices and equity market in the U.S.: A regime switching model," Energy Economics, Elsevier, vol. 39(C), pages 271-282.
    33. Chiang, Thomas C. & Jeon, Bang Nam & Li, Huimin, 2007. "Dynamic correlation analysis of financial contagion: Evidence from Asian markets," Journal of International Money and Finance, Elsevier, vol. 26(7), pages 1206-1228, November.
    34. Berger, Theo, 2015. "A wavelet based approach to measure and manage contagion at different time scales," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 338-350.
    35. Pircalabu, A. & Hvolby, T. & Jung, J. & Høg, E., 2017. "Joint price and volumetric risk in wind power trading: A copula approach," Energy Economics, Elsevier, vol. 62(C), pages 139-154.
    36. Anca Pircalabu & Jesper Jung, 2017. "A mixed C-vine copula model for hedging price and volumetric risk in wind power trading," Quantitative Finance, Taylor & Francis Journals, vol. 17(10), pages 1583-1600, October.
    37. U. Cherubini & E. Luciano, 2002. "Bivariate option pricing with copulas," Applied Mathematical Finance, Taylor & Francis Journals, vol. 9(2), pages 69-85.
    38. 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.
    39. Sadorsky, Perry, 1999. "Oil price shocks and stock market activity," Energy Economics, Elsevier, vol. 21(5), pages 449-469, October.
    40. Filis, George, 2010. "Macro economy, stock market and oil prices: Do meaningful relationships exist among their cyclical fluctuations?," Energy Economics, Elsevier, vol. 32(4), pages 877-886, July.
    41. 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.
    42. Hansen, Bruce E, 1994. "Autoregressive Conditional Density Estimation," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 35(3), pages 705-730, August.
    43. François Longin & Bruno Solnik, 2001. "Extreme Correlation of International Equity Markets," Journal of Finance, American Finance Association, vol. 56(2), pages 649-676, April.
    44. Cong, Rong-Gang & Wei, Yi-Ming & Jiao, Jian-Lin & Fan, Ying, 2008. "Relationships between oil price shocks and stock market: An empirical analysis from China," Energy Policy, Elsevier, vol. 36(9), pages 3544-3553, September.
    45. Jammazi, Rania & Reboredo, Juan C., 2016. "Dependence and risk management in oil and stock markets. A wavelet-copula analysis," Energy, Elsevier, vol. 107(C), pages 866-888.
    46. Reboredo, Juan C. & Ugolini, Andrea, 2016. "Quantile dependence of oil price movements and stock returns," Energy Economics, Elsevier, vol. 54(C), pages 33-49.
    47. Tong, Bin & Wu, Chongfeng & Zhou, Chunyang, 2013. "Modeling the co-movements between crude oil and refined petroleum markets," Energy Economics, Elsevier, vol. 40(C), pages 882-897.
    48. Abadie A., 2002. "Bootstrap Tests for Distributional Treatment Effects in Instrumental Variable Models," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 284-292, March.
    49. Nandha, Mohan & Faff, Robert, 2008. "Does oil move equity prices? A global view," Energy Economics, Elsevier, vol. 30(3), pages 986-997, May.
    50. Mensi, Walid & Tiwari, Aviral & Bouri, Elie & Roubaud, David & Al-Yahyaee, Khamis H., 2017. "The dependence structure across oil, wheat, and corn: A wavelet-based copula approach using implied volatility indexes," Energy Economics, Elsevier, vol. 66(C), pages 122-139.
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    More about this item

    Keywords

    Crude oil market; China stock market; Variational mode decomposition; Copula; CoVaR;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy

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