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Dynamic Correlation between Crude Oil Price and Investor Sentiment in China: Heterogeneous and Asymmetric Effect

Author

Listed:
  • Zhenghui Li

    (Guangzhou Institute of International Finance, Guangzhou University, Guangzhou 510006, China)

  • Zimei Huang

    (School of Economics and Statistics, Guangzhou University, Guangzhou 510006, China)

  • Pierre Failler

    (Department of Economics and Finance, Portsmouth Business School, University of Portsmouth, Portsmouth PO1 3DE, UK)

Abstract

This paper aims to explore the dynamic relationships between the crude oil price (shocks) and investor sentiment. Specifically, this paper utilizes web crawler to construct Chinese investor sentiment index. The structural vector autoregression (SVAR) model is then used to decompose the crude oil price shocks into three types of oil price shocks. Finally, the wavelet coherence analysis (WTC) is employed to study the dynamic correlation between crude oil price (shocks) and investor sentiment in the time and frequency domain, and their asymmetric dynamic correlation under different trends of crude oil price. Using data from February 2013 to June 2021, our empirical results suggest the heterogeneous dynamic correlations and lead-lag relationships exist between crude oil price (shocks) and investor sentiment over different time and frequency domains. In addition, there are asymmetric dynamic correlations and lead–lag relationships between crude oil price (shocks) and investor sentiment under different trends of crude oil price.

Suggested Citation

  • Zhenghui Li & Zimei Huang & Pierre Failler, 2022. "Dynamic Correlation between Crude Oil Price and Investor Sentiment in China: Heterogeneous and Asymmetric Effect," Energies, MDPI, vol. 15(3), pages 1-22, January.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:3:p:687-:d:727442
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    Citations

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    Cited by:

    1. Shan Huang & Yan Ding & Pierre Failler, 2022. "Does the Government’s Environmental Attention Affect Ambient Pollution? Empirical Research on Chinese Cities," Sustainability, MDPI, vol. 14(6), pages 1-19, March.
    2. Renchu Guan & Aoqing Wang & Yanchun Liang & Jiasheng Fu & Xiaosong Han, 2022. "International Natural Gas Price Trends Prediction with Historical Prices and Related News," Energies, MDPI, vol. 15(10), pages 1-14, May.
    3. Marcus Vinicius Santos & Fernando Morgado-Dias & Thiago C. Silva, 2023. "Oil Sector and Sentiment Analysis—A Review," Energies, MDPI, vol. 16(12), pages 1-29, June.
    4. Bei Zhang & Xiaoqing Ai & Xingming Fang & Shi Chen, 2022. "The Transmission Mechanisms and Impacts of Oil Price Fluctuations: Evidence from DSGE Model," Energies, MDPI, vol. 15(16), pages 1-20, August.
    5. Aharon, David Y. & Azman Aziz, Mukhriz Izraf & Kallir, Ido, 2023. "Oil price shocks and inflation: A cross-national examination in the ASEAN5+3 countries," Resources Policy, Elsevier, vol. 82(C).
    6. Kaiming Zhong & Hongyan Fu & Tinghui Li, 2022. "Can the Digital Economy Facilitate Carbon Emissions Decoupling? An Empirical Study Based on Provincial Data in China," IJERPH, MDPI, vol. 19(11), pages 1-25, June.
    7. Yanhong Feng & Xiaolei Wang & Shuanglian Chen & Yanqiong Liu, 2022. "Impact of Oil Financialization on Oil Price Fluctuation: A Perspective of Heterogeneity," Energies, MDPI, vol. 15(12), pages 1-20, June.
    8. Villa-Loaiza, Carlos & Taype-Huaman, Irvin & Benavides-Franco, Julián & Buenaventura-Vera, Guillermo & Carabalí-Mosquera, Jaime, 2023. "Does climate impact the relationship between the energy price and the stock market? The Colombian case," Applied Energy, Elsevier, vol. 336(C).
    9. Shuai Wang & Cunyi Yang & Zhenghui Li, 2022. "Green Total Factor Productivity Growth: Policy-Guided or Market-Driven?," IJERPH, MDPI, vol. 19(17), pages 1-19, August.
    10. Nikolaos Rodousakis & George Soklis & Theodore Tsekeris, 2022. "A Supply and Use Model for Estimating the Contribution of Costs to Energy Prices," Energies, MDPI, vol. 15(19), pages 1-10, September.
    11. Yan Ding & Yue Liu & Pierre Failler, 2022. "The Impact of Uncertainties on Crude Oil Prices: Based on a Quantile-on-Quantile Method," Energies, MDPI, vol. 15(10), pages 1-35, May.
    12. Yanling Li & Mengxin Wang & Gaoke Liao & Junxia Wang, 2022. "Spatial Spillover Effect and Threshold Effect of Digital Financial Inclusion on Farmers’ Income Growth—Based on Provincial Data of China," Sustainability, MDPI, vol. 14(3), pages 1-16, February.
    13. Amged Al Ezzi & Miqdam T. Chaichan & Hasan S. Majdi & Ali H. A. Al-Waeli & Hussein A. Kazem & Kamaruzzaman Sopian & Mohammed A. Fayad & Hayder A. Dhahad & Talal Yusaf, 2022. "Nano-Iron Oxide-Ethylene Glycol-Water Nanofluid Based Photovoltaic Thermal (PV/T) System with Spiral Flow Absorber: An Energy and Exergy Analysis," Energies, MDPI, vol. 15(11), pages 1-19, May.
    14. Sa Xu & Cunyi Yang & Zhehao Huang & Pierre Failler, 2022. "Interaction between Digital Economy and Environmental Pollution: New Evidence from a Spatial Perspective," IJERPH, MDPI, vol. 19(9), pages 1-23, April.
    15. Armenia Androniceanu & Oana Matilda Sabie, 2022. "Overview of Green Energy as a Real Strategic Option for Sustainable Development," Energies, MDPI, vol. 15(22), pages 1-35, November.
    16. Yue Liu & Pierre Failler & Zhiying Liu, 2022. "Impact of Environmental Regulations on Energy Efficiency: A Case Study of China’s Air Pollution Prevention and Control Action Plan," Sustainability, MDPI, vol. 14(6), pages 1-21, March.
    17. Saad Balhasan & Mohammed Alnahhal & Brian Towler & Bashir Salah & Mohammed Ruzayqat & Mosab I. Tabash, 2022. "Robust Exploration and Production Sharing Agreements Using the Taguchi Method," Energies, MDPI, vol. 15(15), pages 1-19, July.

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