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Comparison of historically simulated VaR: Evidence from oil prices

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  • Costello, Alexandra
  • Asem, Ebenezer
  • Gardner, Eldon

Abstract

Cabedo and Moya [Cabedo, J.D., Moya, I., 2003. Estimating oil price 'Value at Risk' using the historical simulation approach. Energy Economics 25, 239-253] find that ARMA with historical simulation delivers VaR forecasts that are superior to those from GARCH. We compare the ARMA with historical simulation to the semi-parametric GARCH model proposed by Barone-Adesi et al. [Barone-Adesi, G., Giannopoulos, K., Vosper, L., 1999. VaR without correlations for portfolios of derivative securities. Journal of Futures Markets 19 (5), 583-602]. The results suggest that the semi-parametric GARCH model generates VaR forecasts that are superior to the VaR forecasts from the ARMA with historical simulation. This is due to the fact that GARCH captures volatility clustering. Our findings suggest that Cabedo and Moya's conclusion is mainly driven by the normal distributional assumption imposed on the future risk structure in the GARCH model.

Suggested Citation

  • Costello, Alexandra & Asem, Ebenezer & Gardner, Eldon, 2008. "Comparison of historically simulated VaR: Evidence from oil prices," Energy Economics, Elsevier, vol. 30(5), pages 2154-2166, September.
  • Handle: RePEc:eee:eneeco:v:30:y:2008:i:5:p:2154-2166
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    References listed on IDEAS

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    1. Giot, Pierre & Laurent, Sebastien, 2003. "Market risk in commodity markets: a VaR approach," Energy Economics, Elsevier, vol. 25(5), pages 435-457, September.
    2. David Cabedo, J. & Moya, Ismael, 2003. "Estimating oil price 'Value at Risk' using the historical simulation approach," Energy Economics, Elsevier, vol. 25(3), pages 239-253, May.
    3. Darryll Hendricks, 1996. "Evaluation of value-at-risk models using historical data," Economic Policy Review, Federal Reserve Bank of New York, issue Apr, pages 39-69.
    4. Christoffersen, Peter F, 1998. "Evaluating Interval Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 841-862, November.
    5. Morana, Claudio, 2001. "A semiparametric approach to short-term oil price forecasting," Energy Economics, Elsevier, vol. 23(3), pages 325-338, May.
    6. Robert F. Engle & Simone Manganelli, 1999. "CAViaR: Conditional Value at Risk by Quantile Regression," NBER Working Papers 7341, National Bureau of Economic Research, Inc.
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    Cited by:

    1. Marimoutou, Velayoudoum & Raggad, Bechir & Trabelsi, Abdelwahed, 2009. "Extreme Value Theory and Value at Risk: Application to oil market," Energy Economics, Elsevier, vol. 31(4), pages 519-530, July.
    2. Guo, Zi-Yi, 2017. "Models with Short-Term Variations and Long-Term Dynamics in Risk Management of Commodity Derivatives," EconStor Preprints 167619, ZBW - German National Library of Economics.
    3. repec:eee:eneeco:v:66:y:2017:i:c:p:523-534 is not listed on IDEAS
    4. Zi-Yi Guo, 2017. "A Stochastic Factor Model for Risk Management of Commodity Derivatives," Proceedings of Economics and Finance Conferences 4507452, International Institute of Social and Economic Sciences.
    5. Youssef, Manel & Belkacem, Lotfi & Mokni, Khaled, 2015. "Value-at-Risk estimation of energy commodities: A long-memory GARCH–EVT approach," Energy Economics, Elsevier, vol. 51(C), pages 99-110.
    6. Alex Huang, 2013. "Value at risk estimation by quantile regression and kernel estimator," Review of Quantitative Finance and Accounting, Springer, vol. 41(2), pages 225-251, August.
    7. 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.
    8. Paraschiv, Florentina & Mudry, Pierre-Antoine & Andries, Alin Marius, 2015. "Stress-testing for portfolios of commodity futures," Economic Modelling, Elsevier, vol. 50(C), pages 9-18.
    9. Christos Agiakloglou & Charalampos Agiropoulos, 2011. "The sensitivity of Value-at-Risk estimates using Monte Carlo approach," SPOUDAI Journal of Economics and Business, SPOUDAI Journal of Economics and Business, University of Piraeus, vol. 61(1-2), pages 7-12, January -.
    10. He, Kaijian & Lai, Kin Keung & Yen, Jerome, 2011. "Value-at-risk estimation of crude oil price using MCA based transient risk modeling approach," Energy Economics, Elsevier, vol. 33(5), pages 903-911, September.
    11. Nomikos, Nikos K. & Pouliasis, Panos K., 2011. "Forecasting petroleum futures markets volatility: The role of regimes and market conditions," Energy Economics, Elsevier, vol. 33(2), pages 321-337, March.
    12. Manel Youssef & Lotfi Belkacem & Khaled Mokni, 2015. "Extreme Value Theory and long-memory-GARCH Framework: Application to Stock Market," International Journal of Economics and Empirical Research (IJEER), The Economics and Social Development Organization (TESDO), vol. 3(8), pages 371-388, August.
    13. 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.
    14. Milan Rippel & Ivo Jánský, 2011. "Value at Risk forecasting with the ARMA-GARCH family of models in times of increased volatility," Working Papers IES 2011/27, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, revised Jul 2011.
    15. Gao, Xiangyun & An, Haizhong & Fang, Wei & Li, Huajiao & Sun, Xiaoqi, 2014. "The transmission of fluctuant patterns of the forex burden based on international crude oil prices," Energy, Elsevier, vol. 73(C), pages 380-386.
    16. Chang, Ting-Huan & Su, Hsin-Mei & Chiu, Chien-Liang, 2011. "Value-at-risk estimation with the optimal dynamic biofuel portfolio," Energy Economics, Elsevier, vol. 33(2), pages 264-272, March.

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