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Oil price risk evaluation using a novel hybrid model based on time-varying long memory

Author

Listed:
  • Zhao, Lu-Tao
  • Liu, Kun
  • Duan, Xin-Lei
  • Li, Ming-Fang

Abstract

The volatility of crude oil price has a great influence on the world economy. In order to measure the crude oil price risk (VaR) and explain the dynamic relationship between investment income and risk in the oil market more clearly, this paper uses a variety of fractional GARCH models to describe typical volatility characteristics like long memory, volatility clustering, asymmetry and thick tail. The autoregressive conditional heteroscedasticity in the mean model (ARCH-M) and peaks-over-threshold model of extreme value theory (EVT-POT) are taken into account to develop a hybrid time-varying long memory GARCH-M-EVT model for calculation of static and dynamic VaR. Empirical results show that the WTI crude oil has a significantly long memory feature. All the fractional integration GARCH models can describe the long memory appropriately and the FIAPARCH model is the best in regression and out of sample one-step-ahead VaR forecasting. Back-testing results show that the FIAPARCH-M-EVT model is superior to other GARCH-type models which only consider oil price fluctuation characteristics partially and traditional methods including Variance-Covariance and Monte Carlo in price risk measurement. Our conclusions confirm that considering long memory, asymmetry and fat tails in the behavior of energy commodity return combined with effectively dynamic time-varying risk reflection such as the ARCH-M model and reliable tail extreme filter processes such as EVT can improve the accuracy of crude oil price risk measurement, provide an effective tool for analyzing the extreme risk of the tail of the oil market and facilitate the risk management for oil market investors.

Suggested Citation

  • Zhao, Lu-Tao & Liu, Kun & Duan, Xin-Lei & Li, Ming-Fang, 2019. "Oil price risk evaluation using a novel hybrid model based on time-varying long memory," Energy Economics, Elsevier, vol. 81(C), pages 70-78.
    • Handle: RePEc:eee:eneeco:v:81:y:2019:i:c:p:70-78
    DOI: 10.1016/j.eneco.2019.03.019
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    Citations

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    Cited by:

    1. Wen, Jun & Zhao, Xin-Xin & Chang, Chun-Ping, 2021. "The impact of extreme events on energy price risk," Energy Economics, Elsevier, vol. 99(C).
    2. Kunal Saha & Vinodh Madhavan & Chandrashekhar G. R. & David McMillan, 2020. "Pitfalls in long memory research," Cogent Economics & Finance, Taylor & Francis Journals, vol. 8(1), pages 1733280-173, January.
    3. Pushpa Dissanayake & Teresa Flock & Johanna Meier & Philipp Sibbertsen, 2021. "Modelling Short- and Long-Term Dependencies of Clustered High-Threshold Exceedances in Significant Wave Heights," Mathematics, MDPI, vol. 9(21), pages 1-33, November.
    4. H. Kaibuchi & Y. Kawasaki & G. Stupfler, 2022. "GARCH-UGH: a bias-reduced approach for dynamic extreme Value-at-Risk estimation in financial time series," Quantitative Finance, Taylor & Francis Journals, vol. 22(7), pages 1277-1294, July.
    5. Paulo F. Marschner & Paulo Sergio Ceretta, 2021. "The impact of oil price shocks on latin american stock markets: a behavioral approach," Economics Bulletin, AccessEcon, vol. 41(2), pages 457-467.
    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, Linna & Lei, Yalin & Yang, Yang & Zheng, Haoqi & Wang, Wen & Meng, Yan & Meng, Chunhong & Zha, Liqiang, 2023. "Assessing nickel sector index volatility based on quantile regression for Garch and Egarch models: Evidence from the Chinese stock market 2018–2022," Resources Policy, Elsevier, vol. 82(C).
    8. Lahmiri, Salim & Bekiros, Stelios, 2021. "The effect of COVID-19 on long memory in returns and volatility of cryptocurrency and stock markets," Chaos, Solitons & Fractals, Elsevier, vol. 151(C).
    9. Chen, Xuehui & Zhu, Hongli & Zhang, Xinru & Zhao, Lutao, 2022. "A novel time-varying FIGARCH model for improving volatility predictions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 589(C).
    10. Jingjian, Si & Xiangyun, Gao & Jinsheng, Zhou & Anjian, Wang & Xiaotian, Sun & Yiran, Zhao & Hongyu, Wei, 2023. "The impact of oil price shocks on energy stocks from the perspective of investor attention," Energy, Elsevier, vol. 278(PB).
    11. Lu-Tao Zhao & Guan-Rong Zeng & Wen-Jing Wang & Zhi-Gang Zhang, 2019. "Forecasting Oil Price Using Web-based Sentiment Analysis," Energies, MDPI, vol. 12(22), pages 1-18, November.
    12. Yanqiong Liu & Zhenghui Li & Yanyan Yao & Hao Dong, 2021. "Asymmetry of Risk Evolution in Crude Oil Market: From the Perspective of Dual Attributes of Oil," Energies, MDPI, vol. 14(13), pages 1-22, July.
    13. Yonghong Jiang & Jinqi Mu & He Nie & Lanxin Wu, 2022. "Time‐frequency analysis of risk spillovers from oil to BRICS stock markets: A long‐memory Copula‐CoVaR‐MODWT method," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(3), pages 3386-3404, July.
    14. Lu Yang & Shigeyuki Hamori, 2020. "Forecasts of Value-at-Risk and Expected Shortfall in the Crude Oil Market: A Wavelet-Based Semiparametric Approach," Energies, MDPI, vol. 13(14), pages 1-27, July.
    15. Liu, Siyao & Fang, Wei & Gao, Xiangyun & Wang, Ze & An, Feng & Wen, Shaobo, 2020. "Self-similar behaviors in the crude oil market," Energy, Elsevier, vol. 211(C).

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