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A study of lead–lag structure between international crude oil price and several financial markets

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  • Yao, Can-Zhong
  • Kuang, Peng-Cheng

Abstract

Complex non-linear relationship between international crude oil price and financial market has challenged the classical econometric method. This paper studies the relationships between oil price and several financial markets based on both the Copula model and the Thermal Optimal Path method. First, we investigate the tail dependence of copula function between crude oil market and financial market. The results demonstrate that there were different correlations at several time periods. In 2013 and 2014, the risk caused by oil price volatilities could be reduced by diversified investment in the U.S. and China stock markets. After 2015, the tail dependence between crude oil market and two stock markets tended to converge, and the effect of multi-national investment strategy was weakened. Furthermore, we make a comparison with two kinds of cross-correlation curves, respectively of price sequence and of return sequence. The price evolution mechanism of stock market is predicted while the stock returns in various countries are more heterogeneous. Finally, we employ the thermal optimal path method to characterize the dynamic lead–lag relationships. The lead–lag structure between oil market and the U.S. stock market has stronger signal than that between oil market and China stock market, and the return spillover effect of oil market might show diverse pattern in mature or emerging stock market. During 2000 to 2002, the U.S. stock market led oil market with a leading time about 20 weeks, and subsequently the significant lead–lag structure occurred in the mid-2008.

Suggested Citation

  • Yao, Can-Zhong & Kuang, Peng-Cheng, 2019. "A study of lead–lag structure between international crude oil price and several financial markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 531(C).
    • Handle: RePEc:eee:phsmap:v:531:y:2019:i:c:s0378437119310453
    DOI: 10.1016/j.physa.2019.121755
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    Citations

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    Cited by:

    1. Yang, Yan-Hong & Shao, Ying-Hui, 2020. "Time-dependent lead-lag relationships between the VIX and VIX futures markets," The North American Journal of Economics and Finance, Elsevier, vol. 53(C).
    2. Jilong Chen & Christian Ewald & Ruolan Ouyang & Sjur Westgaard & Xiaoxia Xiao, 2022. "Pricing commodity futures and determining risk premia in a three factor model with stochastic volatility: the case of Brent crude oil," Annals of Operations Research, Springer, vol. 313(1), pages 29-46, June.
    3. Donghua Wang & Tianhui Fang, 2022. "Forecasting Crude Oil Prices with a WT-FNN Model," Energies, MDPI, vol. 15(6), pages 1-21, March.
    4. Kuang, Peng-Cheng, 2021. "Measuring information flow among international stock markets: An approach of entropy-based networks on multi time-scales," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 577(C).
    5. Long Zhang & Wuliyasu Bai, 2020. "Risk Assessment of China’s Natural Gas Importation: A Supply Chain Perspective," SAGE Open, , vol. 10(3), pages 21582440209, July.
    6. Salisu, Afees A. & Adediran, Idris & Omoke, Philip C. & Tchankam, Jean Paul, 2023. "Gold and tail risks," Resources Policy, Elsevier, vol. 80(C).
    7. Saeed, Tareq & Bouri, Elie & Alsulami, Hamed, 2021. "Extreme return connectedness and its determinants between clean/green and dirty energy investments," Energy Economics, Elsevier, vol. 96(C).
    8. Indrė Lapinskaitė & Algita Miečinskienė, 2019. "Assessment of the Impact of Hard Commodity Prices Changes on Inflation in European Union Countries," Central European Business Review, Prague University of Economics and Business, vol. 2019(5), pages 18-35.
    9. Fang, Tianhui & Zheng, Chunling & Wang, Donghua, 2023. "Forecasting the crude oil prices with an EMD-ISBM-FNN model," Energy, Elsevier, vol. 263(PA).
    10. Yao, Can-Zhong & Li, Hong-Yu, 2020. "Time-varying lead–lag structure between investor sentiment and stock market," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).

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