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The spillover and comovement of downside and upside tail risks among crude oil futures markets

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  • Yang, Jie
  • Feng, Yun
  • Yang, Hao

Abstract

Combining efficient transfer entropy and DY spillover indices, this study first explores the contagion effects of downside and upside tail risks among INE, Brent, and WTI. Then we utilize wavelet coherence to capture the comovement characteristics of tail risks at multi-time scales. Furthermore, by mapping wavelet coherence information into comovement networks, the diversity and regularity of mode conversions of tail interdependency are investigated in depth. The results show that INE is reactively susceptible to the tail risk spillovers from Brent and WTI, and in terms of the magnitude of spillover to INE, Brent surpasses WTI prominently. The coherence between the tail risks of INE and Brent or WTI is much weaker than that of the Brent-WTI pair, especially in the short term or in the upside tail case. The evolution of downside tail risk comovement is more difficult to stabilize. A few comovement modes show a significantly higher probability of occurrence than others, and they also tend to link each other. Of special note are some comovement modes with low probability of occurrence yet behave as the key “bridges” in the mode transformation process.

Suggested Citation

  • Yang, Jie & Feng, Yun & Yang, Hao, 2024. "The spillover and comovement of downside and upside tail risks among crude oil futures markets," International Review of Financial Analysis, Elsevier, vol. 96(PA).
  • Handle: RePEc:eee:finana:v:96:y:2024:i:pa:s1057521924005106
    DOI: 10.1016/j.irfa.2024.103578
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    as
    1. Sang Hoon Kang & Seong‐Min Yoon, 2020. "Dynamic correlation and volatility spillovers across Chinese stock and commodity futures markets," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 25(2), pages 261-273, April.
    2. 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.
    3. Jozef Baruník & Tomáš Křehlík, 2018. "Measuring the Frequency Dynamics of Financial Connectedness and Systemic Risk," Journal of Financial Econometrics, Oxford University Press, vol. 16(2), pages 271-296.
    4. Jiang, Yonghong & Nie, He & Monginsidi, Joe Yohanes, 2017. "Co-movement of ASEAN stock markets: New evidence from wavelet and VMD-based copula tests," Economic Modelling, Elsevier, vol. 64(C), pages 384-398.
    5. Liu, Min & Lee, Chien-Chiang, 2021. "Capturing the dynamics of the China crude oil futures: Markov switching, co-movement, and volatility forecasting," Energy Economics, Elsevier, vol. 103(C).
    6. Yue‐Jun Zhang & Shu‐Jiao Ma, 2021. "Exploring the dynamic price discovery, risk transfer and spillover among INE, WTI and Brent crude oil futures markets: Evidence from the high‐frequency data," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(2), pages 2414-2435, April.
    7. Huang, Xiaohong & Huang, Shupei, 2020. "Identifying the comovement of price between China's and international crude oil futures: A time-frequency perspective," International Review of Financial Analysis, Elsevier, vol. 72(C).
    8. Huang, Dashan & Yu, Baimin & Fabozzi, Frank J. & Fukushima, Masao, 2009. "CAViaR-based forecast for oil price risk," Energy Economics, Elsevier, vol. 31(4), pages 511-518, July.
    9. Wen, Fenghua & Cao, Jiahui & Liu, Zhen & Wang, Xiong, 2021. "Dynamic volatility spillovers and investment strategies between the Chinese stock market and commodity markets," International Review of Financial Analysis, Elsevier, vol. 76(C).
    10. LI, Jie & HUANG, Lixin & LI, Ping, 2021. "Are Chinese crude oil futures good hedging tools?," Finance Research Letters, Elsevier, vol. 38(C).
    11. Zhu, Pengfei & Tang, Yong & Wei, Yu & Dai, Yimin & Lu, Tuantuan, 2021. "Relationships and portfolios between oil and Chinese stock sectors: A study based on wavelet denoising-higher moments perspective," Energy, Elsevier, vol. 217(C).
    12. Bei, Shuhua & Yang, Aijun & Pei, Haotian & Si, Xiaoli, 2023. "Price Risk Analysis using GARCH Family Models: Evidence from Shanghai Crude Oil Futures Market," Economic Modelling, Elsevier, vol. 125(C).
    13. Sun, Chuanwang & Peng, Yiqi & Zhan, Yanhong, 2023. "How does China's crude oil futures affect the crude oil prices at home and abroad? Evidence from the cross-market exchange rate spillovers," International Review of Economics & Finance, Elsevier, vol. 88(C), pages 204-222.
    14. Robert F. Engle & Simone Manganelli, 2004. "CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles," Journal of Business & Economic Statistics, American Statistical Association, vol. 22, pages 367-381, October.
    15. An, Haizhong & Gao, Xiangyun & Fang, Wei & Huang, Xuan & Ding, Yinghui, 2014. "The role of fluctuating modes of autocorrelation in crude oil prices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 393(C), pages 382-390.
    16. Duan, Kun & Ren, Xiaohang & Wen, Fenghua & Chen, Jinyu, 2023. "Evolution of the information transmission between Chinese and international oil markets: A quantile-based framework," Journal of Commodity Markets, Elsevier, vol. 29(C).
    17. Lee, Chien-Chiang & Zhou, Hegang & Xu, Chao & Zhang, Xiaoming, 2023. "Dynamic spillover effects among international crude oil markets from the time-frequency perspective," Resources Policy, Elsevier, vol. 80(C).
    18. Wei, Yu & Zhang, Yaojie & Wang, Yudong, 2022. "Information connectedness of international crude oil futures: Evidence from SC, WTI, and Brent," International Review of Financial Analysis, Elsevier, vol. 81(C).
    19. Wang, Jianli & Qiu, Shushu & Yick, Ho Yin, 2022. "The influence of the Shanghai crude oil futures on the global and domestic oil markets," Energy, Elsevier, vol. 245(C).
    20. Naqvi, Bushra & Mirza, Nawazish & Umar, Muhammad & Rizvi, Syed Kumail Abbas, 2023. "Shanghai crude oil futures: Returns Independence, volatility asymmetry, and hedging potential," Energy Economics, Elsevier, vol. 128(C).
    21. Luís Aguiar-Conraria & Maria Joana Soares, 2014. "The Continuous Wavelet Transform: Moving Beyond Uni- And Bivariate Analysis," Journal of Economic Surveys, Wiley Blackwell, vol. 28(2), pages 344-375, April.
    22. Jian Yang & Yinggang Zhou, 2020. "Return and volatility transmission between China's and international crude oil futures markets: A first look," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 40(6), pages 860-884, June.
    23. Chatziantoniou, Ioannis & Gabauer, David & Perez de Gracia, Fernando, 2022. "Tail risk connectedness in the refined petroleum market: A first look at the impact of the COVID-19 pandemic," Energy Economics, Elsevier, vol. 111(C).
    24. Diebold, Francis X. & Yilmaz, Kamil, 2012. "Better to give than to receive: Predictive directional measurement of volatility spillovers," International Journal of Forecasting, Elsevier, vol. 28(1), pages 57-66.
    25. Tae-Hwy Lee & Yong Bao & Burak Saltoglu, 2006. "Evaluating predictive performance of value-at-risk models in emerging markets: a reality check," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(2), pages 101-128.
    26. Fang, Tianhui & Zheng, Chunling & Wang, Donghua, 2023. "Forecasting the crude oil prices with an EMD-ISBM-FNN model," Energy, Elsevier, vol. 263(PA).
    27. Li, Panpan & Dong, Zhiliang, 2020. "Time-varying network analysis of fluctuations between crude oil and Chinese and U.S. gold prices in different periods," Resources Policy, Elsevier, vol. 68(C).
    28. Wang, Suhui, 2023. "Tail dependence, dynamic linkages, and extreme spillover between the stock and China's commodity markets," Journal of Commodity Markets, Elsevier, vol. 29(C).
    29. Hu, Yang & Lang, Chunlin & Corbet, Shaen & Hou, Yang (Greg) & Oxley, Les, 2023. "Exploring the dynamic behaviour of commodity market tail risk connectedness during the negative WTI pricing event," Energy Economics, Elsevier, vol. 125(C).
    30. Zhu, Pengfei & Tang, Yong & Wei, Yu & Lu, Tuantuan, 2021. "Multidimensional risk spillovers among crude oil, the US and Chinese stock markets: Evidence during the COVID-19 epidemic," Energy, Elsevier, vol. 231(C).
    31. Chen, Hao & Sun, Zesheng, 2021. "International crude oil price, regulation and asymmetric response of China's gasoline price," Energy Economics, Elsevier, vol. 94(C).
    32. Qiang Ji & Dayong Zhang & Yuqian Zhao, 2022. "Intra-day co-movements of crude oil futures: China and the international benchmarks," Annals of Operations Research, Springer, vol. 313(1), pages 77-103, June.
    33. Chaohua He & Guangchen Li & Hai Fan & Weixian Wei, 2021. "Correlation between Shanghai crude oil futures, stock, foreign exchange, and gold markets: a GARCH-vine-copula method," Applied Economics, Taylor & Francis Journals, vol. 53(11), pages 1249-1263, March.
    34. Palao, Fernando & Pardo, Ángel & Roig, Marta, 2020. "Is the leadership of the Brent-WTI threatened by China’s new crude oil futures market?," Journal of Asian Economics, Elsevier, vol. 70(C).
    35. Yang, Chen & Lv, Fei & Fang, Libing & Shang, Xingxing, 2020. "The pricing efficiency of crude oil futures in the Shanghai International Exchange," Finance Research Letters, Elsevier, vol. 36(C).
    36. Jiang, Zhe & Zhang, Lin & Zhang, Lingling & Wen, Bo, 2022. "Investor sentiment and machine learning: Predicting the price of China's crude oil futures market," Energy, Elsevier, vol. 247(C).
    37. 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.
    38. Zhang, Hongwei & Jin, Chen & Bouri, Elie & Gao, Wang & Xu, Yahua, 2023. "Realized higher-order moments spillovers between commodity and stock markets: Evidence from China," Journal of Commodity Markets, Elsevier, vol. 30(C).
    39. Yang, Yuying & Ma, Yan-Ran & Hu, Min & Zhang, Dayong & Ji, Qiang, 2021. "Extreme risk spillover between chinese and global crude oil futures," Finance Research Letters, Elsevier, vol. 40(C).
    40. Gong, Xu & Xu, Jun, 2022. "Geopolitical risk and dynamic connectedness between commodity markets," Energy Economics, Elsevier, vol. 110(C).
    41. Naeem, Muhammad Abubakr & Gul, Raazia & Shafiullah, Muhammad & Karim, Sitara & Lucey, Brian M., 2024. "Tail risk spillovers between Shanghai oil and other markets," Energy Economics, Elsevier, vol. 130(C).
    42. Sensoy, Ahmet & Sobaci, Cihat & Sensoy, Sadri & Alali, Fatih, 2014. "Effective transfer entropy approach to information flow between exchange rates and stock markets," Chaos, Solitons & Fractals, Elsevier, vol. 68(C), pages 180-185.
    43. Shi, Xunpeng & Sun, Sizhong, 2017. "Energy price, regulatory price distortion and economic growth: A case study of China," Energy Economics, Elsevier, vol. 63(C), pages 261-271.
    44. Shao, Mingao & Hua, Yongjun, 2022. "Price discovery efficiency of China's crude oil futures: Evidence from the Shanghai crude oil futures market," Energy Economics, Elsevier, vol. 112(C).
    45. Lin, Boqiang & Su, Tong, 2021. "Do China's macro-financial factors determine the Shanghai crude oil futures market?," International Review of Financial Analysis, Elsevier, vol. 78(C).
    46. Sun, Guanglin & Li, Jianfeng & Shang, Zezhong, 2022. "Return and volatility linkages between international energy markets and Chinese commodity market," Technological Forecasting and Social Change, Elsevier, vol. 179(C).
    47. Feng, Yun & Yang, Jie & Huang, Qian, 2023. "Multiscale correlation analysis of Sino-US corn futures markets and the impact of international crude oil price: A new perspective from the multifractal method," Finance Research Letters, Elsevier, vol. 53(C).
    48. Zhang, Dayong & Shi, Min & Shi, Xunpeng, 2018. "Oil indexation, market fundamentals, and natural gas prices: An investigation of the Asian premium in natural gas trade," Energy Economics, Elsevier, vol. 69(C), pages 33-41.
    49. Lucey, Brian & Ren, Boru, 2023. "Time-varying tail risk connectedness among sustainability-related products and fossil energy investments," Energy Economics, Elsevier, vol. 126(C).
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    More about this item

    Keywords

    Crude oil futures; Tail risk; Efficient transfer entropy; Wavelet coherence; Comovement network;
    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
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy

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