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The impact of investor sentiment on crude oil market risks: evidence from the wavelet approach


  • Yue-Jun Zhang
  • Shu-Hui Li


Investor sentiment has become an important factor affecting oil price volatility and extreme risk. Therefore, we utilise a VaR-GARCH model to detect the extreme risk of the crude oil market during 2007–2017, and then explore the causality between investor sentiment and extreme risk in the crude oil market, and their lead-lag and co-movement relationships in the time-frequency domain. The empirical results show that: firstly, investor sentiment leads downside risk but lags the upside risk in the crude oil market; secondly, in the time domain, there is a co-movement between investor sentiment and extreme risk in the crude oil market, in particular, investor sentiment may Granger cause extreme risk in the crude oil market at the 1% significance level but not vice versa; thirdly, in the frequency domain, weak coherence can be found in high-frequency bands but increases in low-frequency bands during the whole sample period, which indicates that the impact of investor sentiment on extreme risk in the crude oil market will last for a long time, although the affected period tends to decrease.

Suggested Citation

  • Yue-Jun Zhang & Shu-Hui Li, 2019. "The impact of investor sentiment on crude oil market risks: evidence from the wavelet approach," Quantitative Finance, Taylor & Francis Journals, vol. 19(8), pages 1357-1371, August.
  • Handle: RePEc:taf:quantf:v:19:y:2019:i:8:p:1357-1371
    DOI: 10.1080/14697688.2019.1581368

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    Cited by:

    1. Matteo Bonato & Konstantinos Gkillas & Rangan Gupta & Christian Pierdzioch, 2020. "Investor Happiness and Predictability of the Realized Volatility of Oil Price," Sustainability, MDPI, Open Access Journal, vol. 12(10), pages 1-1, May.
    2. Wei, Yi-Ming & Qiao, Lu & Lv, Xin, 2020. "The impact of mergers and acquisitions on technology learning in the petroleum industry," Energy Economics, Elsevier, vol. 88(C).
    3. Hao, Yu & Gai, Zhiqiang & Wu, Haitao, 2020. "How do resource misallocation and government corruption affect green total factor energy efficiency? Evidence from China," Energy Policy, Elsevier, vol. 143(C).
    4. Zhang, Yue-Jun & Yan, Xing-Xing, 2020. "The impact of US economic policy uncertainty on WTI crude oil returns in different time and frequency domains," International Review of Economics & Finance, Elsevier, vol. 69(C), pages 750-768.
    5. Zhang, Yue-Jun & Ma, Shu-Jiao, 2019. "How to effectively estimate the time-varying risk spillover between crude oil and stock markets? Evidence from the expectile perspective," Energy Economics, Elsevier, vol. 84(C).
    6. Maghyereh, Aktham & Awartani, Basel & Abdoh, Hussein, 2020. "The effects of investor emotions sentiments on crude oil returns: A time and frequency dynamics analysis," International Economics, Elsevier, vol. 162(C), pages 110-124.
    7. Liu, Xueyong & Jiang, Cheng, 2020. "The dynamic volatility transmission in the multiscale spillover network of the international stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 560(C).

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