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Measuring contagion effects between crude oil and Chinese stock market sectors

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  • Fang, Sheng
  • Egan, Paul

Abstract

The role of cross-market linkages in the occurrence of tail events in stock and energy markets has not yet been fully understood in the contagion literature. This paper investigates the contagion from oil prices to Chinese stock sectors by considering differences between extreme positive returns and extreme negative returns. We compute time-varying cut-offs by employing a generalized Pareto distribution (GPD) function to estimate excess returns. We then use a multinomial logit (MNL) model to examine the probability of Chinese stock sector co-exceedances associated with oil price exceedances. Our results indicate that, compared to common domestic factors, the contagion between oil price and stock sectors is relatively weak, but never negligible. We argue that faced with volatile oil prices during turbulent periods, the existence of any contagion weakens the benefits of portfolio diversification related to oil and Chinese stock sector investment. Based on our findings, investors holding a portfolio of oil and Chinese sector stocks should pay special attention to the extreme changes in crude oil prices and adopt hedging measures to protect their portfolio from extreme shocks to oil markets.

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  • Fang, Sheng & Egan, Paul, 2018. "Measuring contagion effects between crude oil and Chinese stock market sectors," The Quarterly Review of Economics and Finance, Elsevier, vol. 68(C), pages 31-38.
    • Handle: RePEc:eee:quaeco:v:68:y:2018:i:c:p:31-38
    DOI: 10.1016/j.qref.2017.11.010
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    2. Billah, Mabruk & Karim, Sitara & Naeem, Muhammad Abubakr & Vigne, Samuel A., 2022. "Return and volatility spillovers between energy and BRIC markets: Evidence from quantile connectedness," Research in International Business and Finance, Elsevier, vol. 62(C).
    3. Hamdi, Besma & Aloui, Mouna & Alqahtani, Faisal & Tiwari, Aviral, 2019. "Relationship between the oil price volatility and sectoral stock markets in oil-exporting economies: Evidence from wavelet nonlinear denoised based quantile and Granger-causality analysis," Energy Economics, Elsevier, vol. 80(C), pages 536-552.
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    5. Jiasha Fu & Hui Qiao, 2022. "The Time-Varying Connectedness Between China’s Crude Oil Futures and International Oil Markets: A Return and Volatility Spillover Analysis," Letters in Spatial and Resource Sciences, Springer, vol. 15(3), pages 341-376, December.
    6. 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).
    7. Syed Jawad Hussain Shahzad & Elie Bouri & Mobeen Ur Rehman & Muhammad Abubakr Naeem & Tareq Saeed, 2022. "Oil price risk exposure of BRIC stock markets and hedging effectiveness," Annals of Operations Research, Springer, vol. 313(1), pages 145-170, June.
    8. Syed Mujahid Hussain & Amjad Naveed & Sheraz Ahmed & Nisar Ahmad, 2022. "Disaggregating the impact of oil prices on European industrial equity indices: a spatial econometric analysis," Empirical Economics, Springer, vol. 62(6), pages 2673-2692, June.
    9. Mensi, Walid & Hanif, Waqas & Vo, Xuan Vinh & Choi, Ki-Hong & Yoon, Seong-Min, 2023. "Upside/Downside spillovers between oil and Chinese stock sectors: From the global financial crisis to global pandemic," The North American Journal of Economics and Finance, Elsevier, vol. 67(C).
    10. Kuang, Wei, 2023. "The equity-oil hedge: A comparison between volatility and alternative risk frameworks," Energy, Elsevier, vol. 271(C).
    11. Aviral Kumar Tiwari & Samia Nasreen & Subhan Ullah & Muhammad Shahbaz, 2021. "Analysing spillover between returns and volatility series of oil across major stock markets," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(2), pages 2458-2490, April.
    12. Jingran Zhu & Qinghua Song & Dalia Streimikiene, 2020. "Multi-Time Scale Spillover Effect of International Oil Price Fluctuation on China’s Stock Markets," Energies, MDPI, vol. 13(18), pages 1-29, September.
    13. Zhao, Zhao & Wen, Huwei & Li, Ke, 2021. "Identifying bubbles and the contagion effect between oil and stock markets: New evidence from China," Economic Modelling, Elsevier, vol. 94(C), pages 780-788.
    14. Zhao-Yong Sun & Wei-Chiao Huang, 2023. "The effects of unexpected crude oil price shocks on Chinese stock markets," Economic Change and Restructuring, Springer, vol. 56(3), pages 1683-1697, June.
    15. Sheng Fang & Paul Egan, 2021. "Tail dependence between oil prices and China's A‐shares: Evidence from firm‐level data," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(1), pages 1469-1487, January.
    16. Ghaemi Asl, Mahdi & Adekoya, Oluwasegun Babatunde & Rashidi, Muhammad Mahdi & Ghasemi Doudkanlou, Mohammad & Dolatabadi, Ali, 2022. "Forecast of Bayesian-based dynamic connectedness between oil market and Islamic stock indices of Islamic oil-exporting countries: Application of the cascade-forward backpropagation network," Resources Policy, Elsevier, vol. 77(C).
    17. Stoupos, Nikolaos & Kiohos, Apostolos, 2021. "Energy commodities and advanced stock markets: A post-crisis approach," Resources Policy, Elsevier, vol. 70(C).
    18. Ma, Yan-Ran & Zhang, Dayong & Ji, Qiang & Pan, Jiaofeng, 2019. "Spillovers between oil and stock returns in the US energy sector: Does idiosyncratic information matter?," Energy Economics, Elsevier, vol. 81(C), pages 536-544.
    19. Somayeh Kokabisaghi & Mohammadesmaeil Ezazi & Reza Tehrani & Nourmohammad Yaghoubi, 2019. "Sanction or Financial Crisis? An Artificial Neural Network-Based Approach to model the impact of oil price volatility on Stock and industry indices," Papers 1912.04015, arXiv.org, revised Sep 2020.
    20. Chen, Bin-xia & Sun, Yan-lin, 2023. "Extreme risk contagion between international crude oil and China's energy-intensive sectors: New evidence from quantile Granger causality and spillover methods," Energy, Elsevier, vol. 285(C).
    21. Mishra, Aswini Kumar & Ghate, Kshitish, 2022. "Dynamic connectedness in non-ferrous commodity markets: Evidence from India using TVP-VAR and DCC-GARCH approaches," Resources Policy, Elsevier, vol. 76(C).

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    More about this item

    Keywords

    C32; G12; G15; Contagion; Oil market; Chinese stock sectors; Extreme returns; Co-exceedances;
    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
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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