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Estimating 'Value at Risk' of crude oil price and its spillover effect using the GED-GARCH approach

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  • Fan, Ying
  • Zhang, Yue-Jun
  • Tsai, Hsien-Tang
  • Wei, Yi-Ming

Abstract

Estimation has been carried out using GARCH-type models, based on the Generalized Error Distribution (GED), for both the extreme downside and upside Value-at-Risks (VaR) of returns in the WTI and Brent crude oil spot markets. Furthermore, according to a new concept of Granger causality in risk, a kernel-based test is proposed to detect extreme risk spillover effect between the two oil markets. Results of an empirical study indicate that the GED-GARCH-based VaR approach appears more effective than the well-recognized HSAF (i.e. historical simulation with ARMA forecasts). Moreover, this approach is also more realistic and comprehensive than the standard normal distribution-based VaR model that is commonly used. Results reveal that there is significant two-way risk spillover effect between WTI and Brent markets. Supplementary study indicates that at the 99% confidence level, when negative market news arises that brings about a slump in oil price return, historical information on risk in the WTI market helps to forecast the Brent market. Conversely, it is not the case when positive news occurs and returns rise. Historical information on risk in the two markets can facilitate forecasts of future extreme market risks for each other. These results are valuable for anyone who needs evaluation and forecasts of the risk situation in international crude oil markets.

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Bibliographic Info

Article provided by Elsevier in its journal Energy Economics.

Volume (Year): 30 (2008)
Issue (Month): 6 (November)
Pages: 3156-3171

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Handle: RePEc:eee:eneeco:v:30:y:2008:i:6:p:3156-3171

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Web page: http://www.elsevier.com/locate/eneco

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Keywords: International crude oil markets GED-GARCH models Value-at-Risk (VaR) Granger causality in risk Risk spillover effect;

References

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Cited by:
  1. Lu, Feng-bin & Hong, Yong-miao & Wang, Shou-yang & Lai, Kin-keung & Liu, John, 2014. "Time-varying Granger causality tests for applications in global crude oil markets," Energy Economics, Elsevier, vol. 42(C), pages 289-298.
  2. Marc Joëts, 2012. "Energy price transmissions during extreme movements," EconomiX Working Papers 2012-38, University of Paris West - Nanterre la Défense, EconomiX.
  3. de Araújo, André da Silva & Garcia, Maria Teresa Medeiros, 2013. "Risk contagion in the north-western and southern European stock markets," Journal of Economics and Business, Elsevier, vol. 69(C), pages 1-34.
  4. repec:ipg:wpaper:28 is not listed on IDEAS
  5. Med Imen Gallali & Raggad Zahraa, 2012. "Evaluation of VaR models' forecasting performance: the case of oil markets," International Journal of Financial Services Management, Inderscience Enterprises Ltd, vol. 5(3), pages 197-215.
  6. Chang, Kuang-Liang, 2012. "Volatility regimes, asymmetric basis effects and forecasting performance: An empirical investigation of the WTI crude oil futures market," Energy Economics, Elsevier, vol. 34(1), pages 294-306.
  7. Lei Zhu & ZhongXiang Zhang & Ying Fan, 2011. "An evaluation of overseas oil investment projects under uncertainty using a real options based simulation model," Economics Study Area Working Papers 121, East-West Center, Economics Study Area.
  8. Ghorbel, Ahmed & Trabelsi, Abdelwahed, 2014. "Energy portfolio risk management using time-varying extreme value copula methods," Economic Modelling, Elsevier, vol. 38(C), pages 470-485.
  9. Wang, Yudong & Wu, Chongfeng, 2012. "Forecasting energy market volatility using GARCH models: Can multivariate models beat univariate models?," Energy Economics, Elsevier, vol. 34(6), pages 2167-2181.
  10. Herrera, Rodrigo, 2013. "Energy risk management through self-exciting marked point process," Energy Economics, Elsevier, vol. 38(C), pages 64-76.
  11. Wu, Chih-Chiang & Chung, Huimin & Chang, Yu-Hsien, 2012. "The economic value of co-movement between oil price and exchange rate using copula-based GARCH models," Energy Economics, Elsevier, vol. 34(1), pages 270-282.
  12. Chang, Kuang-Liang, 2012. "The time-varying and asymmetric dependence between crude oil spot and futures markets: Evidence from the Mixture copula-based ARJI–GARCH model," Economic Modelling, Elsevier, vol. 29(6), pages 2298-2309.
  13. Ladislav Kristoufek, 2014. "Leverage effect in energy futures," Papers 1403.0064, arXiv.org.
  14. Jian Zhou, 2013. "Extreme risk spillover among international REIT markets," Applied Financial Economics, Taylor & Francis Journals, vol. 23(2), pages 91-103, January.

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