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Measuring tail risk with GAS time varying copula, fat tailed GARCH model and hedging for crude oil futures

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  • Gong, Xiao-Li
  • Liu, Xi-Hua
  • Xiong, Xiong

Abstract

Considering the leptokurtic feature and clustering effect of returns distribution in portfolio as well as the nonlinear dependence structure among multiple variables of financial assets, the crude oil futures returns are assumed to follow Skew t distribution, with the asymmetric GJR-GARCH-Skew t model used to characterize the marginal distribution of crude oil futures returns. By utilizing the generalized autoregressive score (GAS) method to update copula function parameters over time, the GAS time varying copula model is employed to describe the nonlinear dependence among futures returns variables. Then the GJR-GARCH-Skew t-GAS copula model is constructed for the crude oil futures markets to investigate the fitting performances of marginal distributions combining with time-varying copula models. In addition, we modify the previous two-stage estimation method with modified quasi-maximum likelihood estimator for the GARCH model with heavy tailed innovation error. Furthermore, we utilize the newly constructed model to analyze the tail dependence and to measure the portfolio risk for crude oil futures markets, along with calculating the dynamic hedge ratio for crude oil spot. Empirical studies have found that the Brent and WTI crude oil futures exhibit higher peakness, thick tails and persistent volatility, which are suitable for the GJR-GARCH-Skew t marginal distribution. Connecting with constant and time-varying copulas functions, the tail dependence and portfolio risk of VaR and ES are investigated. It illustrates that the GAS Rotated Gumbel copula captures the tail behaviors best, with the corresponding dynamic tail dependence and risk measurements computed. Moreover, we compare the dynamic hedging efficiency of the crude oil futures employing different GAS copulas to enlighten investors.

Suggested Citation

  • Gong, Xiao-Li & Liu, Xi-Hua & Xiong, Xiong, 2019. "Measuring tail risk with GAS time varying copula, fat tailed GARCH model and hedging for crude oil futures," Pacific-Basin Finance Journal, Elsevier, vol. 55(C), pages 95-109.
    • Handle: RePEc:eee:pacfin:v:55:y:2019:i:c:p:95-109
    DOI: 10.1016/j.pacfin.2019.03.010
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    Citations

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    Cited by:

    1. Guo, Dong & Zhou, Peng, 2021. "Green bonds as hedging assets before and after COVID: A comparative study between the US and China," Energy Economics, Elsevier, vol. 104(C).
    2. Chang, Kuang-Liang, 2023. "The low-magnitude and high-magnitude asymmetries in tail dependence structures in international equity markets and the role of bilateral exchange rate," Journal of International Money and Finance, Elsevier, vol. 133(C).
    3. Haiying Wang & Ying Yuan & Tianyang Wang, 2021. "The dynamics of cross‐boundary fire—Financial contagion between the oil and stock markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(10), pages 1655-1673, October.
    4. Gong, Xiao-Li & Zhao, Min & Wu, Zhuo-Cheng & Jia, Kai-Wen & Xiong, Xiong, 2023. "Research on tail risk contagion in international energy markets—The quantile time-frequency volatility spillover perspective," Energy Economics, Elsevier, vol. 121(C).
    5. Héctor Alonso Olivares Aguayo, 2021. "Portafolios mexicanos tradicionales y no tradicionales," Revista de Investigación en Ciencias Contables y Administrativas, Universidad Michoacana de San Nicolás de Hidalgo, Facultad de Contaduría y Ciencias Administrativas, vol. 6(2), pages 3-25, July.
    6. 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).
    7. Kakade, Kshitij & Jain, Ishan & Mishra, Aswini Kumar, 2022. "Value-at-Risk forecasting: A hybrid ensemble learning GARCH-LSTM based approach," Resources Policy, Elsevier, vol. 78(C).
    8. Zhu, Pengfei & Lu, Tuantuan & Chen, Shenglan, 2022. "How do crude oil futures hedge crude oil spot risk after the COVID-19 outbreak? A wavelet denoising-GARCHSK-SJC Copula hedge ratio estimation method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).
    9. Gong, Xiao-Li & Xiong, Xiong, 2021. "Multi-objective portfolio optimization under tempered stable Lévy distribution with Copula dependence," Finance Research Letters, Elsevier, vol. 38(C).
    10. Wu, Chih-Chiang & Chen, Wei-Peng & Korsakul, Nattawadee, 2021. "Extreme linkages between foreign exchange and general financial markets," Pacific-Basin Finance Journal, Elsevier, vol. 65(C).
    11. Gong, Xiao-Li & Liu, Xi-Hua & Xiong, Xiong & Zhang, Wei, 2020. "Research on China's financial systemic risk contagion under jump and heavy-tailed risk," International Review of Financial Analysis, Elsevier, vol. 72(C).
    12. Giuseppe Orlando & Michele Bufalo, 2021. "Empirical Evidences on the Interconnectedness between Sampling and Asset Returns’ Distributions," Risks, MDPI, vol. 9(5), pages 1-35, May.
    13. Gong, Xiao-Li & Feng, Yong-Kang & Liu, Jian-Min & Xiong, Xiong, 2023. "Study on international energy market and geopolitical risk contagion based on complex network," Resources Policy, Elsevier, vol. 82(C).
    14. Héctor Alonso Olivares Aguayo, 2021. "Portafolios mexicanos tradicionales y no tradicionales," Revista de Investigación en Ciencias Contables y Administrativas, Universidad Michoacana de San Nicolás de Hidalgo, Facultad de Contaduría y Ciencias Administrativas, vol. 6(2), pages 3-25, July.

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