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Risk assessment of oil price from static and dynamic modelling approaches

Author

Listed:
  • Zhi-Fu Mi
  • Yi-Ming Wei
  • Bao-Jun Tang
  • Rong-Gang Cong
  • Hao Yu
  • Hong Cao
  • Dabo Guan

Abstract

The price gap between West Texas Intermediate (WTI) and Brent crude oil markets has been completely changed in the past several years. The price of WTI was always a little larger than that of Brent for a long time. However, the price of WTI has been surpassed by that of Brent since 2011. The new market circumstances and volatility of oil price require a comprehensive re-estimation of risk. Therefore, this study aims to explore an integrated approach to assess the price risk in the two crude oil markets through the value at risk (VaR) model. The VaR is estimated by the extreme value theory (EVT) and GARCH model on the basis of generalized error distribution (GED). The results show that EVT is a powerful approach to capture the risk in the oil markets. On the contrary, the traditional variance–covariance (VC) and Monte Carlo (MC) approaches tend to overestimate risk when the confidence level is 95%, but underestimate risk at the confidence level of 99%. The VaR of WTI returns is larger than that of Brent returns at identical confidence levels. Moreover, the GED-GARCH model can estimate the downside dynamic VaR accurately for WTI and Brent oil returns.

Suggested Citation

  • Zhi-Fu Mi & Yi-Ming Wei & Bao-Jun Tang & Rong-Gang Cong & Hao Yu & Hong Cao & Dabo Guan, 2017. "Risk assessment of oil price from static and dynamic modelling approaches," Applied Economics, Taylor & Francis Journals, vol. 49(9), pages 929-939, February.
    • Handle: RePEc:taf:applec:v:49:y:2017:i:9:p:929-939
    DOI: 10.1080/00036846.2016.1208359
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    Cited by:

    1. Yue-Jun Zhang & Han Zhang, 2023. "Volatility Forecasting of Crude Oil Market: Which Structural Change Based GARCH Models have Better Performance?," The Energy Journal, , vol. 44(1), pages 175-194, January.
    2. Hui-Ling Zhou & Bao-Jun Tang & Hong Cao, 2020. "Abandonment Decision-Making of Overseas Oilfield Project Coping with Low Oil Price," Computational Economics, Springer;Society for Computational Economics, vol. 55(4), pages 1171-1184, April.
    3. Wilson Calmon & Eduardo Ferioli & Davi Lettieri & Johann Soares & Adrian Pizzinga, 2021. "An Extensive Comparison of Some Well‐Established Value at Risk Methods," International Statistical Review, International Statistical Institute, vol. 89(1), pages 148-166, April.
    4. Zhang, Yue-Jun & Yao, Ting & He, Ling-Yun & Ripple, Ronald, 2019. "Volatility forecasting of crude oil market: Can the regime switching GARCH model beat the single-regime GARCH models?," International Review of Economics & Finance, Elsevier, vol. 59(C), pages 302-317.
    5. Chen, Yan & Zhang, Lei & Zhang, Feipeng, 2024. "Forecasting crude oil volatility and stock volatility: New evidence from the quantile autoregressive model," The North American Journal of Economics and Finance, Elsevier, vol. 74(C).
    6. Ra l De Jes s Guti rrez & Lidia E. Carvajal Guti rrez & Oswaldo Garcia Salgado, 2023. "Value at Risk and Expected Shortfall Estimation for Mexico s Isthmus Crude Oil Using Long-Memory GARCH-EVT Combined Approaches," International Journal of Energy Economics and Policy, Econjournals, vol. 13(4), pages 467-480, July.
    7. Lu-Tao Zhao & Li-Na Liu & Zi-Jie Wang & Ling-Yun He, 2019. "Forecasting Oil Price Volatility in the Era of Big Data: A Text Mining for VaR Approach," Sustainability, MDPI, vol. 11(14), pages 1-20, July.
    8. Zhang, Yue-Jun & Chevallier, Julien & Guesmi, Khaled, 2017. "“De-financialization” of commodities? Evidence from stock, crude oil and natural gas markets," Energy Economics, Elsevier, vol. 68(C), pages 228-239.
    9. F. Benedetto & L. Mastroeni & P. Vellucci, 2021. "Modeling the flow of information between financial time-series by an entropy-based approach," Annals of Operations Research, Springer, vol. 299(1), pages 1235-1252, April.
    10. Zhang, Lei & Chen, Yan & Bouri, Elie, 2024. "Time-varying jump intensity and volatility forecasting of crude oil returns," Energy Economics, Elsevier, vol. 129(C).
    11. Pavel Kotyza & Katarzyna Czech & Michał Wielechowski & Luboš Smutka & Petr Procházka, 2021. "Sugar Prices vs. Financial Market Uncertainty in the Time of Crisis: Does COVID-19 Induce Structural Changes in the Relationship?," Agriculture, MDPI, vol. 11(2), pages 1-16, January.
    12. Gong, Xu & Lin, Boqiang, 2017. "Forecasting the good and bad uncertainties of crude oil prices using a HAR framework," Energy Economics, Elsevier, vol. 67(C), pages 315-327.
    13. 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.
    14. Jianfeng Guo & Bin Su & Guang Yang & Lianyong Feng & Yinpeng Liu & Fu Gu, 2018. "How Do Verified Emissions Announcements Affect the Comoves between Trading Behaviors and Carbon Prices? Evidence from EU ETS," Sustainability, MDPI, vol. 10(9), pages 1-17, September.
    15. Rui Zha & Lean Yu & Xi Xi & Yi Su, 2025. "Risk Estimation in the Bitcoin Market Using a Three-Stage Ensemble Method," Computational Economics, Springer;Society for Computational Economics, vol. 66(4), pages 3473-3496, October.
    16. Yao, Ting & Zhang, Yue-Jun & Ma, Chao-Qun, 2017. "How does investor attention affect international crude oil prices?," Applied Energy, Elsevier, vol. 205(C), pages 336-344.
    17. Pierluigi Vellucci, 2021. "A critique of financial neoliberalism: a perspective combining multidisciplinary methods and commodity markets," SN Business & Economics, Springer, vol. 1(3), pages 1-11, March.
    18. Tang, Bao-Jun & Zhou, Hui-Ling & Chen, Hao & Wang, Kai & Cao, Hong, 2017. "Investment opportunity in China's overseas oil project: An empirical analysis based on real option approach," Energy Policy, Elsevier, vol. 105(C), pages 17-26.
    19. Bin Xu & Boqiang Lin, 2021. "Large fluctuations of China's commodity prices: Main sources and heterogeneous effects," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(2), pages 2074-2089, April.

    More about this item

    JEL classification:

    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming
    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General

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